• So, Disney Dreamlight Valley is at it again with their “emotional rescue” update, proving that even virtual characters need therapy. Who knew cartoon critters had such complicated feelings? And now, they’re throwing in “Vice Versa” – because nothing says emotional depth like a magical valley where you can swap your personality traits like trading Pokémon cards.

    Remember when games were just about adventure? Now, it’s all about saving emotions, one pixel at a time! Can’t wait to see how many emotional breakdowns it takes before we get a refund on our childhoods.

    #DisneyDreamlightValley #EmotionalRescue #ViceVersa #GamingSatire #NostalgiaTrip
    So, Disney Dreamlight Valley is at it again with their “emotional rescue” update, proving that even virtual characters need therapy. Who knew cartoon critters had such complicated feelings? And now, they’re throwing in “Vice Versa” – because nothing says emotional depth like a magical valley where you can swap your personality traits like trading Pokémon cards. Remember when games were just about adventure? Now, it’s all about saving emotions, one pixel at a time! Can’t wait to see how many emotional breakdowns it takes before we get a refund on our childhoods. #DisneyDreamlightValley #EmotionalRescue #ViceVersa #GamingSatire #NostalgiaTrip
    Disney Dreamlight Valley Sauvetage Emotionnel, la mise à jour gratuite ajoutera Vice Versa
    www.actugaming.net
    ActuGaming.net Disney Dreamlight Valley Sauvetage Emotionnel, la mise à jour gratuite ajoutera Vice Versa Depuis maintenant trois ans, Disney Dreamlight Valley continue son petit bonhomme de chemin, en étant […] L'article Disney Dreamlight Val
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  • Ah, the joy of gaming in 2025! Who would have thought that one of the most “special” and “emotional” experiences you could have would be a $5 game that you can finish in an hour? I mean, why spend hours on an epic saga when you can have a complete emotional breakdown in just 60 minutes?

    Forget about investing in deep narratives or expansive worlds. Just grab your wallet, drop some change, and prepare for a whirlwind of feelings that might leave you questioning your life choices—because nothing screams “art” like a budget title with a time crunch!

    So, if you're ready to embrace the profound complexities of a game that you’ll probably forget by lunchtime, jump on board the emotional rollerco
    Ah, the joy of gaming in 2025! Who would have thought that one of the most “special” and “emotional” experiences you could have would be a $5 game that you can finish in an hour? I mean, why spend hours on an epic saga when you can have a complete emotional breakdown in just 60 minutes? Forget about investing in deep narratives or expansive worlds. Just grab your wallet, drop some change, and prepare for a whirlwind of feelings that might leave you questioning your life choices—because nothing screams “art” like a budget title with a time crunch! So, if you're ready to embrace the profound complexities of a game that you’ll probably forget by lunchtime, jump on board the emotional rollerco
    One Of 2025’s Most Special, Emotional Games Is $5 And Will Take You An Hour To Finish
    kotaku.com
    and Roger is the kind of game you should just play without knowing what it’s about, if you can The post One Of 2025’s Most Special, Emotional Games Is $5 And Will Take You An Hour To Finish appeared first on Kotaku.
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  • Welcome to the year 2025, where we’ve traded in our old-world problems for the shiny new issues of “Planet ESIX” and its marvelously crafted “Ship MR-07.” Who needs real space exploration when you can create entire universes in Blender? Polygonal modeling, you say? How quaint! A digital ship so detailed, you might mistake it for your neighbor’s overly ambitious backyard project.

    Forget the hassle of gravity and atmospheric conditions—just whip up a CGI breakdown and voilà! You’re a space captain in a world where imagination is the only limit. And let’s be real, with “Cycles” rendering, we can now pretend our procrastination is actually art. Cheers to the future!

    #PlanetESIX #Ship
    Welcome to the year 2025, where we’ve traded in our old-world problems for the shiny new issues of “Planet ESIX” and its marvelously crafted “Ship MR-07.” Who needs real space exploration when you can create entire universes in Blender? Polygonal modeling, you say? How quaint! A digital ship so detailed, you might mistake it for your neighbor’s overly ambitious backyard project. Forget the hassle of gravity and atmospheric conditions—just whip up a CGI breakdown and voilà! You’re a space captain in a world where imagination is the only limit. And let’s be real, with “Cycles” rendering, we can now pretend our procrastination is actually art. Cheers to the future! #PlanetESIX #Ship
    www.blendernation.com
    "MR-07" is a cinematic visualization of a futuristic world, entirely created in Blender, with Cycles as the main rendering engine. The core of the scene is a highly detailed sci-fi ship, fully modeled from scratch inside Blender using traditional pol
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  • Hey everyone! The excitement is building as we get closer to the release of James Gunn’s highly anticipated Superman film! After nearly three years of passionate fan theories and trailer breakdowns, the first reactions are rolling in, and they’re… well, mixed! But remember, every opinion is a stepping stone to something greater!

    Let’s keep our hearts open and embrace the thrill of new stories! No matter the critics’ views, what truly matters is the joy of experiencing Clark Kent’s journey once again! So mark your calendars for July 11 and get ready to fly high with Superman!

    Together, let's celebrate creativity and positivity in every frame!

    #Super
    🌟 Hey everyone! The excitement is building as we get closer to the release of James Gunn’s highly anticipated Superman film! 🎬 After nearly three years of passionate fan theories and trailer breakdowns, the first reactions are rolling in, and they’re… well, mixed! But remember, every opinion is a stepping stone to something greater! 💪✨ Let’s keep our hearts open and embrace the thrill of new stories! No matter the critics’ views, what truly matters is the joy of experiencing Clark Kent’s journey once again! 🌈 So mark your calendars for July 11 and get ready to fly high with Superman! 🦸‍♂️ Together, let's celebrate creativity and positivity in every frame! 💖 #Super
    The Early Superman Reactions From Critics Are In, And They're Mixed
    kotaku.com
    After nearly three years of fan theories, trailer breakdowns, and unjustified hate, James Gunn’s Superman film is almost here. While the general public will get to gander at the latest attempt at telling Clark Kent’s story on July 11, film critics an
    1 Commentarios ·0 Acciones ·0 Vista previa
  • What on earth is going on with the VFX in Netflix's "The Snow Sister"? Seriously, it’s 2023, and we’re still being fed mediocre visual effects that are supposed to "wow" us but end up doing the exact opposite! The so-called "VFX breakdown" is nothing more than a slap in the face to anyone who actually appreciates the art of visual storytelling.

    Let’s get one thing straight: if the best VFX are indeed the ones you can’t spot, then how on earth did we end up with these glaringly obvious digital blunders? It’s like they threw a bunch of half-baked effects together and called it a day. Instead of stunning visuals that elevate the narrative, we get a distracting mess that pulls you right out of the experience. Who are they kidding?

    The creators of "The Snow Sister" clearly missed the memo that viewers today are not easily satisfied. We demand more than just passable effects; we want immersive worlds that captivate us. And yet, here we are, subjected to a barrage of poorly executed VFX that look like they belong in a low-budget production from the early 2000s. It’s frustrating to see Netflix, a platform that should be setting the gold standard in content creation, flounder so embarrassingly with something as fundamental as visual effects.

    What’s even more maddening is the disconnect between the promotional hype and the actual product. They tout the "creation" of these effects as if they’re groundbreaking, but in reality, they are a visual cacophony that leaves much to be desired. How can anyone take this seriously when the final product looks like it was hastily patched together? It’s not just a disservice to the viewers; it’s an insult to the talented artists who work tirelessly in the VFX industry. They deserve better than to have their hard work represented by subpar results that manage to undermine the entire project.

    Netflix needs to wake up and understand that audiences are becoming increasingly discerning. We’re not just mindless consumers; we have eyes, and we can see when something is off. The VFX in "The Snow Sister" is a glaring example of what happens when corners are cut and quality is sacrificed for the sake of quantity. We expect innovation, creativity, and, above all, professionalism. Instead, we are fed a half-hearted effort that leaves us shaking our heads in disbelief.

    In conclusion, if Netflix wants to maintain its position as a leader in the entertainment industry, it’s time to step up its game and give us the high-quality VFX that we deserve. No more excuses, no more mediocre breakdowns—just real artistry that enhances our viewing experience. Let’s hold them accountable and demand better!

    #VFX #Netflix #TheSnowSister #VisualEffects #EntertainmentIndustry
    What on earth is going on with the VFX in Netflix's "The Snow Sister"? Seriously, it’s 2023, and we’re still being fed mediocre visual effects that are supposed to "wow" us but end up doing the exact opposite! The so-called "VFX breakdown" is nothing more than a slap in the face to anyone who actually appreciates the art of visual storytelling. Let’s get one thing straight: if the best VFX are indeed the ones you can’t spot, then how on earth did we end up with these glaringly obvious digital blunders? It’s like they threw a bunch of half-baked effects together and called it a day. Instead of stunning visuals that elevate the narrative, we get a distracting mess that pulls you right out of the experience. Who are they kidding? The creators of "The Snow Sister" clearly missed the memo that viewers today are not easily satisfied. We demand more than just passable effects; we want immersive worlds that captivate us. And yet, here we are, subjected to a barrage of poorly executed VFX that look like they belong in a low-budget production from the early 2000s. It’s frustrating to see Netflix, a platform that should be setting the gold standard in content creation, flounder so embarrassingly with something as fundamental as visual effects. What’s even more maddening is the disconnect between the promotional hype and the actual product. They tout the "creation" of these effects as if they’re groundbreaking, but in reality, they are a visual cacophony that leaves much to be desired. How can anyone take this seriously when the final product looks like it was hastily patched together? It’s not just a disservice to the viewers; it’s an insult to the talented artists who work tirelessly in the VFX industry. They deserve better than to have their hard work represented by subpar results that manage to undermine the entire project. Netflix needs to wake up and understand that audiences are becoming increasingly discerning. We’re not just mindless consumers; we have eyes, and we can see when something is off. The VFX in "The Snow Sister" is a glaring example of what happens when corners are cut and quality is sacrificed for the sake of quantity. We expect innovation, creativity, and, above all, professionalism. Instead, we are fed a half-hearted effort that leaves us shaking our heads in disbelief. In conclusion, if Netflix wants to maintain its position as a leader in the entertainment industry, it’s time to step up its game and give us the high-quality VFX that we deserve. No more excuses, no more mediocre breakdowns—just real artistry that enhances our viewing experience. Let’s hold them accountable and demand better! #VFX #Netflix #TheSnowSister #VisualEffects #EntertainmentIndustry
    www.blendernation.com
    Enjoy seeing how the VFX in The Snow Sister were created. As always, the best VFX are the ones you can't spot! Source
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  • Take a Look at Procedural Ivy in This Dreamlike 3D Scene

    3D Artist Nick Carver, known for his outstanding stylized artwork, unveiled a new whimsical scene, showing fascinating procedural ivy.The artist stayed true to his signature style, with dreamlike colors and charming hand-painted aesthetics, featuring richly detailed set dressing and high-quality animation.Earlier, Nick Carver showcased this splendid character study, a peaceful 3D scene with a calm river, and more:Follow the artist on X/Twitter and don't forget to join our 80 Level Talent platform and our new Discord server, follow us on Instagram, Twitter, LinkedIn, Telegram, TikTok, and Threads, where we share breakdowns, the latest news, awesome artworks, and more.
    #take #look #procedural #ivy #this
    Take a Look at Procedural Ivy in This Dreamlike 3D Scene
    3D Artist Nick Carver, known for his outstanding stylized artwork, unveiled a new whimsical scene, showing fascinating procedural ivy.The artist stayed true to his signature style, with dreamlike colors and charming hand-painted aesthetics, featuring richly detailed set dressing and high-quality animation.Earlier, Nick Carver showcased this splendid character study, a peaceful 3D scene with a calm river, and more:Follow the artist on X/Twitter and don't forget to join our 80 Level Talent platform and our new Discord server, follow us on Instagram, Twitter, LinkedIn, Telegram, TikTok, and Threads, where we share breakdowns, the latest news, awesome artworks, and more. #take #look #procedural #ivy #this
    Take a Look at Procedural Ivy in This Dreamlike 3D Scene
    80.lv
    3D Artist Nick Carver, known for his outstanding stylized artwork, unveiled a new whimsical scene, showing fascinating procedural ivy.The artist stayed true to his signature style, with dreamlike colors and charming hand-painted aesthetics, featuring richly detailed set dressing and high-quality animation.Earlier, Nick Carver showcased this splendid character study, a peaceful 3D scene with a calm river, and more:Follow the artist on X/Twitter and don't forget to join our 80 Level Talent platform and our new Discord server, follow us on Instagram, Twitter, LinkedIn, Telegram, TikTok, and Threads, where we share breakdowns, the latest news, awesome artworks, and more.
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  • EPFL Researchers Unveil FG2 at CVPR: A New AI Model That Slashes Localization Errors by 28% for Autonomous Vehicles in GPS-Denied Environments

    Navigating the dense urban canyons of cities like San Francisco or New York can be a nightmare for GPS systems. The towering skyscrapers block and reflect satellite signals, leading to location errors of tens of meters. For you and me, that might mean a missed turn. But for an autonomous vehicle or a delivery robot, that level of imprecision is the difference between a successful mission and a costly failure. These machines require pinpoint accuracy to operate safely and efficiently. Addressing this critical challenge, researchers from the École Polytechnique Fédérale de Lausannein Switzerland have introduced a groundbreaking new method for visual localization during CVPR 2025
    Their new paper, “FG2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching,” presents a novel AI model that significantly enhances the ability of a ground-level system, like an autonomous car, to determine its exact position and orientation using only a camera and a corresponding aerialimage. The new approach has demonstrated a remarkable 28% reduction in mean localization error compared to the previous state-of-the-art on a challenging public dataset.
    Key Takeaways:

    Superior Accuracy: The FG2 model reduces the average localization error by a significant 28% on the VIGOR cross-area test set, a challenging benchmark for this task.
    Human-like Intuition: Instead of relying on abstract descriptors, the model mimics human reasoning by matching fine-grained, semantically consistent features—like curbs, crosswalks, and buildings—between a ground-level photo and an aerial map.
    Enhanced Interpretability: The method allows researchers to “see” what the AI is “thinking” by visualizing exactly which features in the ground and aerial images are being matched, a major step forward from previous “black box” models.
    Weakly Supervised Learning: Remarkably, the model learns these complex and consistent feature matches without any direct labels for correspondences. It achieves this using only the final camera pose as a supervisory signal.

    Challenge: Seeing the World from Two Different Angles
    The core problem of cross-view localization is the dramatic difference in perspective between a street-level camera and an overhead satellite view. A building facade seen from the ground looks completely different from its rooftop signature in an aerial image. Existing methods have struggled with this. Some create a general “descriptor” for the entire scene, but this is an abstract approach that doesn’t mirror how humans naturally localize themselves by spotting specific landmarks. Other methods transform the ground image into a Bird’s-Eye-Viewbut are often limited to the ground plane, ignoring crucial vertical structures like buildings.

    FG2: Matching Fine-Grained Features
    The EPFL team’s FG2 method introduces a more intuitive and effective process. It aligns two sets of points: one generated from the ground-level image and another sampled from the aerial map.

    Here’s a breakdown of their innovative pipeline:

    Mapping to 3D: The process begins by taking the features from the ground-level image and lifting them into a 3D point cloud centered around the camera. This creates a 3D representation of the immediate environment.
    Smart Pooling to BEV: This is where the magic happens. Instead of simply flattening the 3D data, the model learns to intelligently select the most important features along the verticaldimension for each point. It essentially asks, “For this spot on the map, is the ground-level road marking more important, or is the edge of that building’s roof the better landmark?” This selection process is crucial, as it allows the model to correctly associate features like building facades with their corresponding rooftops in the aerial view.
    Feature Matching and Pose Estimation: Once both the ground and aerial views are represented as 2D point planes with rich feature descriptors, the model computes the similarity between them. It then samples a sparse set of the most confident matches and uses a classic geometric algorithm called Procrustes alignment to calculate the precise 3-DoFpose.

    Unprecedented Performance and Interpretability
    The results speak for themselves. On the challenging VIGOR dataset, which includes images from different cities in its cross-area test, FG2 reduced the mean localization error by 28% compared to the previous best method. It also demonstrated superior generalization capabilities on the KITTI dataset, a staple in autonomous driving research.

    Perhaps more importantly, the FG2 model offers a new level of transparency. By visualizing the matched points, the researchers showed that the model learns semantically consistent correspondences without being explicitly told to. For example, the system correctly matches zebra crossings, road markings, and even building facades in the ground view to their corresponding locations on the aerial map. This interpretability is extremenly valuable for building trust in safety-critical autonomous systems.
    “A Clearer Path” for Autonomous Navigation
    The FG2 method represents a significant leap forward in fine-grained visual localization. By developing a model that intelligently selects and matches features in a way that mirrors human intuition, the EPFL researchers have not only shattered previous accuracy records but also made the decision-making process of the AI more interpretable. This work paves the way for more robust and reliable navigation systems for autonomous vehicles, drones, and robots, bringing us one step closer to a future where machines can confidently navigate our world, even when GPS fails them.

    Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter.
    Jean-marc MommessinJean-marc is a successful AI business executive .He leads and accelerates growth for AI powered solutions and started a computer vision company in 2006. He is a recognized speaker at AI conferences and has an MBA from Stanford.Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/AI-Generated Ad Created with Google’s Veo3 Airs During NBA Finals, Slashing Production Costs by 95%Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Highlighted at CVPR 2025: Google DeepMind’s ‘Motion Prompting’ Paper Unlocks Granular Video ControlJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Snowflake Charts New AI Territory: Cortex AISQL & Snowflake Intelligence Poised to Reshape Data AnalyticsJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Exclusive Talk: Joey Conway of NVIDIA on Llama Nemotron Ultra and Open Source Models
    #epfl #researchers #unveil #fg2 #cvpr
    EPFL Researchers Unveil FG2 at CVPR: A New AI Model That Slashes Localization Errors by 28% for Autonomous Vehicles in GPS-Denied Environments
    Navigating the dense urban canyons of cities like San Francisco or New York can be a nightmare for GPS systems. The towering skyscrapers block and reflect satellite signals, leading to location errors of tens of meters. For you and me, that might mean a missed turn. But for an autonomous vehicle or a delivery robot, that level of imprecision is the difference between a successful mission and a costly failure. These machines require pinpoint accuracy to operate safely and efficiently. Addressing this critical challenge, researchers from the École Polytechnique Fédérale de Lausannein Switzerland have introduced a groundbreaking new method for visual localization during CVPR 2025 Their new paper, “FG2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching,” presents a novel AI model that significantly enhances the ability of a ground-level system, like an autonomous car, to determine its exact position and orientation using only a camera and a corresponding aerialimage. The new approach has demonstrated a remarkable 28% reduction in mean localization error compared to the previous state-of-the-art on a challenging public dataset. Key Takeaways: Superior Accuracy: The FG2 model reduces the average localization error by a significant 28% on the VIGOR cross-area test set, a challenging benchmark for this task. Human-like Intuition: Instead of relying on abstract descriptors, the model mimics human reasoning by matching fine-grained, semantically consistent features—like curbs, crosswalks, and buildings—between a ground-level photo and an aerial map. Enhanced Interpretability: The method allows researchers to “see” what the AI is “thinking” by visualizing exactly which features in the ground and aerial images are being matched, a major step forward from previous “black box” models. Weakly Supervised Learning: Remarkably, the model learns these complex and consistent feature matches without any direct labels for correspondences. It achieves this using only the final camera pose as a supervisory signal. Challenge: Seeing the World from Two Different Angles The core problem of cross-view localization is the dramatic difference in perspective between a street-level camera and an overhead satellite view. A building facade seen from the ground looks completely different from its rooftop signature in an aerial image. Existing methods have struggled with this. Some create a general “descriptor” for the entire scene, but this is an abstract approach that doesn’t mirror how humans naturally localize themselves by spotting specific landmarks. Other methods transform the ground image into a Bird’s-Eye-Viewbut are often limited to the ground plane, ignoring crucial vertical structures like buildings. FG2: Matching Fine-Grained Features The EPFL team’s FG2 method introduces a more intuitive and effective process. It aligns two sets of points: one generated from the ground-level image and another sampled from the aerial map. Here’s a breakdown of their innovative pipeline: Mapping to 3D: The process begins by taking the features from the ground-level image and lifting them into a 3D point cloud centered around the camera. This creates a 3D representation of the immediate environment. Smart Pooling to BEV: This is where the magic happens. Instead of simply flattening the 3D data, the model learns to intelligently select the most important features along the verticaldimension for each point. It essentially asks, “For this spot on the map, is the ground-level road marking more important, or is the edge of that building’s roof the better landmark?” This selection process is crucial, as it allows the model to correctly associate features like building facades with their corresponding rooftops in the aerial view. Feature Matching and Pose Estimation: Once both the ground and aerial views are represented as 2D point planes with rich feature descriptors, the model computes the similarity between them. It then samples a sparse set of the most confident matches and uses a classic geometric algorithm called Procrustes alignment to calculate the precise 3-DoFpose. Unprecedented Performance and Interpretability The results speak for themselves. On the challenging VIGOR dataset, which includes images from different cities in its cross-area test, FG2 reduced the mean localization error by 28% compared to the previous best method. It also demonstrated superior generalization capabilities on the KITTI dataset, a staple in autonomous driving research. Perhaps more importantly, the FG2 model offers a new level of transparency. By visualizing the matched points, the researchers showed that the model learns semantically consistent correspondences without being explicitly told to. For example, the system correctly matches zebra crossings, road markings, and even building facades in the ground view to their corresponding locations on the aerial map. This interpretability is extremenly valuable for building trust in safety-critical autonomous systems. “A Clearer Path” for Autonomous Navigation The FG2 method represents a significant leap forward in fine-grained visual localization. By developing a model that intelligently selects and matches features in a way that mirrors human intuition, the EPFL researchers have not only shattered previous accuracy records but also made the decision-making process of the AI more interpretable. This work paves the way for more robust and reliable navigation systems for autonomous vehicles, drones, and robots, bringing us one step closer to a future where machines can confidently navigate our world, even when GPS fails them. Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Jean-marc MommessinJean-marc is a successful AI business executive .He leads and accelerates growth for AI powered solutions and started a computer vision company in 2006. He is a recognized speaker at AI conferences and has an MBA from Stanford.Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/AI-Generated Ad Created with Google’s Veo3 Airs During NBA Finals, Slashing Production Costs by 95%Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Highlighted at CVPR 2025: Google DeepMind’s ‘Motion Prompting’ Paper Unlocks Granular Video ControlJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Snowflake Charts New AI Territory: Cortex AISQL & Snowflake Intelligence Poised to Reshape Data AnalyticsJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Exclusive Talk: Joey Conway of NVIDIA on Llama Nemotron Ultra and Open Source Models #epfl #researchers #unveil #fg2 #cvpr
    EPFL Researchers Unveil FG2 at CVPR: A New AI Model That Slashes Localization Errors by 28% for Autonomous Vehicles in GPS-Denied Environments
    www.marktechpost.com
    Navigating the dense urban canyons of cities like San Francisco or New York can be a nightmare for GPS systems. The towering skyscrapers block and reflect satellite signals, leading to location errors of tens of meters. For you and me, that might mean a missed turn. But for an autonomous vehicle or a delivery robot, that level of imprecision is the difference between a successful mission and a costly failure. These machines require pinpoint accuracy to operate safely and efficiently. Addressing this critical challenge, researchers from the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland have introduced a groundbreaking new method for visual localization during CVPR 2025 Their new paper, “FG2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching,” presents a novel AI model that significantly enhances the ability of a ground-level system, like an autonomous car, to determine its exact position and orientation using only a camera and a corresponding aerial (or satellite) image. The new approach has demonstrated a remarkable 28% reduction in mean localization error compared to the previous state-of-the-art on a challenging public dataset. Key Takeaways: Superior Accuracy: The FG2 model reduces the average localization error by a significant 28% on the VIGOR cross-area test set, a challenging benchmark for this task. Human-like Intuition: Instead of relying on abstract descriptors, the model mimics human reasoning by matching fine-grained, semantically consistent features—like curbs, crosswalks, and buildings—between a ground-level photo and an aerial map. Enhanced Interpretability: The method allows researchers to “see” what the AI is “thinking” by visualizing exactly which features in the ground and aerial images are being matched, a major step forward from previous “black box” models. Weakly Supervised Learning: Remarkably, the model learns these complex and consistent feature matches without any direct labels for correspondences. It achieves this using only the final camera pose as a supervisory signal. Challenge: Seeing the World from Two Different Angles The core problem of cross-view localization is the dramatic difference in perspective between a street-level camera and an overhead satellite view. A building facade seen from the ground looks completely different from its rooftop signature in an aerial image. Existing methods have struggled with this. Some create a general “descriptor” for the entire scene, but this is an abstract approach that doesn’t mirror how humans naturally localize themselves by spotting specific landmarks. Other methods transform the ground image into a Bird’s-Eye-View (BEV) but are often limited to the ground plane, ignoring crucial vertical structures like buildings. FG2: Matching Fine-Grained Features The EPFL team’s FG2 method introduces a more intuitive and effective process. It aligns two sets of points: one generated from the ground-level image and another sampled from the aerial map. Here’s a breakdown of their innovative pipeline: Mapping to 3D: The process begins by taking the features from the ground-level image and lifting them into a 3D point cloud centered around the camera. This creates a 3D representation of the immediate environment. Smart Pooling to BEV: This is where the magic happens. Instead of simply flattening the 3D data, the model learns to intelligently select the most important features along the vertical (height) dimension for each point. It essentially asks, “For this spot on the map, is the ground-level road marking more important, or is the edge of that building’s roof the better landmark?” This selection process is crucial, as it allows the model to correctly associate features like building facades with their corresponding rooftops in the aerial view. Feature Matching and Pose Estimation: Once both the ground and aerial views are represented as 2D point planes with rich feature descriptors, the model computes the similarity between them. It then samples a sparse set of the most confident matches and uses a classic geometric algorithm called Procrustes alignment to calculate the precise 3-DoF (x, y, and yaw) pose. Unprecedented Performance and Interpretability The results speak for themselves. On the challenging VIGOR dataset, which includes images from different cities in its cross-area test, FG2 reduced the mean localization error by 28% compared to the previous best method. It also demonstrated superior generalization capabilities on the KITTI dataset, a staple in autonomous driving research. Perhaps more importantly, the FG2 model offers a new level of transparency. By visualizing the matched points, the researchers showed that the model learns semantically consistent correspondences without being explicitly told to. For example, the system correctly matches zebra crossings, road markings, and even building facades in the ground view to their corresponding locations on the aerial map. This interpretability is extremenly valuable for building trust in safety-critical autonomous systems. “A Clearer Path” for Autonomous Navigation The FG2 method represents a significant leap forward in fine-grained visual localization. By developing a model that intelligently selects and matches features in a way that mirrors human intuition, the EPFL researchers have not only shattered previous accuracy records but also made the decision-making process of the AI more interpretable. This work paves the way for more robust and reliable navigation systems for autonomous vehicles, drones, and robots, bringing us one step closer to a future where machines can confidently navigate our world, even when GPS fails them. Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Jean-marc MommessinJean-marc is a successful AI business executive .He leads and accelerates growth for AI powered solutions and started a computer vision company in 2006. He is a recognized speaker at AI conferences and has an MBA from Stanford.Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/AI-Generated Ad Created with Google’s Veo3 Airs During NBA Finals, Slashing Production Costs by 95%Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Highlighted at CVPR 2025: Google DeepMind’s ‘Motion Prompting’ Paper Unlocks Granular Video ControlJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Snowflake Charts New AI Territory: Cortex AISQL & Snowflake Intelligence Poised to Reshape Data AnalyticsJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Exclusive Talk: Joey Conway of NVIDIA on Llama Nemotron Ultra and Open Source Models
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  • Why Designers Get Stuck In The Details And How To Stop

    You’ve drawn fifty versions of the same screen — and you still hate every one of them. Begrudgingly, you pick three, show them to your product manager, and hear: “Looks cool, but the idea doesn’t work.” Sound familiar?
    In this article, I’ll unpack why designers fall into detail work at the wrong moment, examining both process pitfalls and the underlying psychological reasons, as understanding these traps is the first step to overcoming them. I’ll also share tactics I use to climb out of that trap.
    Reason #1 You’re Afraid To Show Rough Work
    We designers worship detail. We’re taught that true craft equals razor‑sharp typography, perfect grids, and pixel precision. So the minute a task arrives, we pop open Figma and start polishing long before polish is needed.
    I’ve skipped the sketch phase more times than I care to admit. I told myself it would be faster, yet I always ended up spending hours producing a tidy mock‑up when a scribbled thumbnail would have sparked a five‑minute chat with my product manager. Rough sketches felt “unprofessional,” so I hid them.
    The cost? Lost time, wasted energy — and, by the third redo, teammates were quietly wondering if I even understood the brief.
    The real problem here is the habit: we open Figma and start perfecting the UI before we’ve even solved the problem.
    So why do we hide these rough sketches? It’s not just a bad habit or plain silly. There are solid psychological reasons behind it. We often just call it perfectionism, but it’s deeper than wanting things neat. Digging into the psychologyshows there are a couple of flavors driving this:

    Socially prescribed perfectionismIt’s that nagging feeling that everyone else expects perfect work from you, which makes showing anything rough feel like walking into the lion’s den.
    Self-oriented perfectionismWhere you’re the one setting impossibly high standards for yourself, leading to brutal self-criticism if anything looks slightly off.

    Either way, the result’s the same: showing unfinished work feels wrong, and you miss out on that vital early feedback.
    Back to the design side, remember that clients rarely see architects’ first pencil sketches, but these sketches still exist; they guide structural choices before the 3D render. Treat your thumbnails the same way — artifacts meant to collapse uncertainty, not portfolio pieces. Once stakeholders see the upside, roughness becomes a badge of speed, not sloppiness. So, the key is to consciously make that shift:
    Treat early sketches as disposable tools for thinking and actively share them to get feedback faster.

    Reason #2: You Fix The Symptom, Not The Cause
    Before tackling any task, we need to understand what business outcome we’re aiming for. Product managers might come to us asking to enlarge the payment button in the shopping cart because users aren’t noticing it. The suggested solution itself isn’t necessarily bad, but before redesigning the button, we should ask, “What data suggests they aren’t noticing it?” Don’t get me wrong, I’m not saying you shouldn’t trust your product manager. On the contrary, these questions help ensure you’re on the same page and working with the same data.
    From my experience, here are several reasons why users might not be clicking that coveted button:

    Users don’t understand that this step is for payment.
    They understand it’s about payment but expect order confirmation first.
    Due to incorrect translation, users don’t understand what the button means.
    Lack of trust signals.
    Unexpected additional coststhat appear at this stage.
    Technical issues.

    Now, imagine you simply did what the manager suggested. Would you have solved the problem? Hardly.
    Moreover, the responsibility for the unresolved issue would fall on you, as the interface solution lies within the design domain. The product manager actually did their job correctly by identifying a problem: suspiciously, few users are clicking the button.
    Psychologically, taking on this bigger role isn’t easy. It means overcoming the fear of making mistakes and the discomfort of exploring unclear problems rather than just doing tasks. This shift means seeing ourselves as partners who create value — even if it means fighting a hesitation to question product managers— and understanding that using our product logic expertise proactively is crucial for modern designers.
    There’s another critical reason why we, designers, need to be a bit like product managers: the rise of AI. I deliberately used a simple example about enlarging a button, but I’m confident that in the near future, AI will easily handle routine design tasks. This worries me, but at the same time, I’m already gladly stepping into the product manager’s territory: understanding product and business metrics, formulating hypotheses, conducting research, and so on. It might sound like I’m taking work away from PMs, but believe me, they undoubtedly have enough on their plates and are usually more than happy to delegate some responsibilities to designers.
    Reason #3: You’re Solving The Wrong Problem
    Before solving anything, ask whether the problem even deserves your attention.
    During a major home‑screen redesign, our goal was to drive more users into paid services. The initial hypothesis — making service buttons bigger and brighter might help returning users — seemed reasonable enough to test. However, even when A/B testsshowed minimal impact, we continued to tweak those buttons.
    Only later did it click: the home screen isn’t the place to sell; visitors open the app to start, not to buy. We removed that promo block, and nothing broke. Contextual entry points deeper into the journey performed brilliantly. Lesson learned:
    Without the right context, any visual tweak is lipstick on a pig.

    Why did we get stuck polishing buttons instead of stopping sooner? It’s easy to get tunnel vision. Psychologically, it’s likely the good old sunk cost fallacy kicking in: we’d already invested time in the buttons, so stopping felt like wasting that effort, even though the data wasn’t promising.
    It’s just easier to keep fiddling with something familiar than to admit we need a new plan. Perhaps the simple question I should have asked myself when results stalled was: “Are we optimizing the right thing or just polishing something that fundamentally doesn’t fit the user’s primary goal here?” That alone might have saved hours.
    Reason #4: You’re Drowning In Unactionable Feedback
    We all discuss our work with colleagues. But here’s a crucial point: what kind of question do you pose to kick off that discussion? If your go-to is “What do you think?” well, that question might lead you down a rabbit hole of personal opinions rather than actionable insights. While experienced colleagues will cut through the noise, others, unsure what to evaluate, might comment on anything and everything — fonts, button colors, even when you desperately need to discuss a user flow.
    What matters here are two things:

    The question you ask,
    The context you give.

    That means clearly stating the problem, what you’ve learned, and how your idea aims to fix it.
    For instance:
    “The problem is our payment conversion rate has dropped by X%. I’ve interviewed users and found they abandon payment because they don’t understand how the total amount is calculated. My solution is to show a detailed cost breakdown. Do you think this actually solves the problem for them?”

    Here, you’ve stated the problem, shared your insight, explained your solution, and asked a direct question. It’s even better if you prepare a list of specific sub-questions. For instance: “Are all items in the cost breakdown clear?” or “Does the placement of this breakdown feel intuitive within the payment flow?”
    Another good habit is to keep your rough sketches and previous iterations handy. Some of your colleagues’ suggestions might be things you’ve already tried. It’s great if you can discuss them immediately to either revisit those ideas or definitively set them aside.
    I’m not a psychologist, but experience tells me that, psychologically, the reluctance to be this specific often stems from a fear of our solution being rejected. We tend to internalize feedback: a seemingly innocent comment like, “Have you considered other ways to organize this section?” or “Perhaps explore a different structure for this part?” can instantly morph in our minds into “You completely messed up the structure. You’re a bad designer.” Imposter syndrome, in all its glory.
    So, to wrap up this point, here are two recommendations:

    Prepare for every design discussion.A couple of focused questions will yield far more valuable input than a vague “So, what do you think?”.
    Actively work on separating feedback on your design from your self-worth.If a mistake is pointed out, acknowledge it, learn from it, and you’ll be less likely to repeat it. This is often easier said than done. For me, it took years of working with a psychotherapist. If you struggle with this, I sincerely wish you strength in overcoming it.

    Reason #5 You’re Just Tired
    Sometimes, the issue isn’t strategic at all — it’s fatigue. Fussing over icon corners can feel like a cozy bunker when your brain is fried. There’s a name for this: decision fatigue. Basically, your brain’s battery for hard thinking is low, so it hides out in the easy, comfy zone of pixel-pushing.
    A striking example comes from a New York Times article titled “Do You Suffer From Decision Fatigue?.” It described how judges deciding on release requests were far more likely to grant release early in the daycompared to late in the daysimply because their decision-making energy was depleted. Luckily, designers rarely hold someone’s freedom in their hands, but the example dramatically shows how fatigue can impact our judgment and productivity.
    What helps here:

    Swap tasks.Trade tickets with another designer; novelty resets your focus.
    Talk to another designer.If NDA permits, ask peers outside the team for a sanity check.
    Step away.Even a ten‑minute walk can do more than a double‑shot espresso.

    By the way, I came up with these ideas while walking around my office. I was lucky to work near a river, and those short walks quickly turned into a helpful habit.

    And one more trick that helps me snap out of detail mode early: if I catch myself making around 20 little tweaks — changing font weight, color, border radius — I just stop. Over time, it turned into a habit. I have a similar one with Instagram: by the third reel, my brain quietly asks, “Wait, weren’t we working?” Funny how that kind of nudge saves a ton of time.
    Four Steps I Use to Avoid Drowning In Detail
    Knowing these potential traps, here’s the practical process I use to stay on track:
    1. Define the Core Problem & Business Goal
    Before anything, dig deep: what’s the actual problem we’re solving, not just the requested task or a surface-level symptom? Ask ‘why’ repeatedly. What user pain or business need are we addressing? Then, state the clear business goal: “What metric am I moving, and do we have data to prove this is the right lever?” If retention is the goal, decide whether push reminders, gamification, or personalised content is the best route. The wrong lever, or tackling a symptom instead of the cause, dooms everything downstream.
    2. Choose the MechanicOnce the core problem and goal are clear, lock the solution principle or ‘mechanic’ first. Going with a game layer? Decide if it’s leaderboards, streaks, or badges. Write it down. Then move on. No UI yet. This keeps the focus high-level before diving into pixels.
    3. Wireframe the Flow & Get Focused Feedback
    Now open Figma. Map screens, layout, and transitions. Boxes and arrows are enough. Keep the fidelity low so the discussion stays on the flow, not colour. Crucially, when you share these early wires, ask specific questions and provide clear contextto get actionable feedback, not just vague opinions.
    4. Polish the VisualsI only let myself tweak grids, type scales, and shadows after the flow is validated. If progress stalls, or before a major polish effort, I surface the work in a design critique — again using targeted questions and clear context — instead of hiding in version 47. This ensures detailing serves the now-validated solution.
    Even for something as small as a single button, running these four checkpoints takes about ten minutes and saves hours of decorative dithering.
    Wrapping Up
    Next time you feel the pull to vanish into mock‑ups before the problem is nailed down, pause and ask what you might be avoiding. Yes, that can expose an uncomfortable truth. But pausing to ask what you might be avoiding — maybe the fuzzy core problem, or just asking for tough feedback — gives you the power to face the real issue head-on. It keeps the project focused on solving the right problem, not just perfecting a flawed solution.
    Attention to detail is a superpower when used at the right moment. Obsessing over pixels too soon, though, is a bad habit and a warning light telling us the process needs a rethink.
    #why #designers #get #stuck #details
    Why Designers Get Stuck In The Details And How To Stop
    You’ve drawn fifty versions of the same screen — and you still hate every one of them. Begrudgingly, you pick three, show them to your product manager, and hear: “Looks cool, but the idea doesn’t work.” Sound familiar? In this article, I’ll unpack why designers fall into detail work at the wrong moment, examining both process pitfalls and the underlying psychological reasons, as understanding these traps is the first step to overcoming them. I’ll also share tactics I use to climb out of that trap. Reason #1 You’re Afraid To Show Rough Work We designers worship detail. We’re taught that true craft equals razor‑sharp typography, perfect grids, and pixel precision. So the minute a task arrives, we pop open Figma and start polishing long before polish is needed. I’ve skipped the sketch phase more times than I care to admit. I told myself it would be faster, yet I always ended up spending hours producing a tidy mock‑up when a scribbled thumbnail would have sparked a five‑minute chat with my product manager. Rough sketches felt “unprofessional,” so I hid them. The cost? Lost time, wasted energy — and, by the third redo, teammates were quietly wondering if I even understood the brief. The real problem here is the habit: we open Figma and start perfecting the UI before we’ve even solved the problem. So why do we hide these rough sketches? It’s not just a bad habit or plain silly. There are solid psychological reasons behind it. We often just call it perfectionism, but it’s deeper than wanting things neat. Digging into the psychologyshows there are a couple of flavors driving this: Socially prescribed perfectionismIt’s that nagging feeling that everyone else expects perfect work from you, which makes showing anything rough feel like walking into the lion’s den. Self-oriented perfectionismWhere you’re the one setting impossibly high standards for yourself, leading to brutal self-criticism if anything looks slightly off. Either way, the result’s the same: showing unfinished work feels wrong, and you miss out on that vital early feedback. Back to the design side, remember that clients rarely see architects’ first pencil sketches, but these sketches still exist; they guide structural choices before the 3D render. Treat your thumbnails the same way — artifacts meant to collapse uncertainty, not portfolio pieces. Once stakeholders see the upside, roughness becomes a badge of speed, not sloppiness. So, the key is to consciously make that shift: Treat early sketches as disposable tools for thinking and actively share them to get feedback faster. Reason #2: You Fix The Symptom, Not The Cause Before tackling any task, we need to understand what business outcome we’re aiming for. Product managers might come to us asking to enlarge the payment button in the shopping cart because users aren’t noticing it. The suggested solution itself isn’t necessarily bad, but before redesigning the button, we should ask, “What data suggests they aren’t noticing it?” Don’t get me wrong, I’m not saying you shouldn’t trust your product manager. On the contrary, these questions help ensure you’re on the same page and working with the same data. From my experience, here are several reasons why users might not be clicking that coveted button: Users don’t understand that this step is for payment. They understand it’s about payment but expect order confirmation first. Due to incorrect translation, users don’t understand what the button means. Lack of trust signals. Unexpected additional coststhat appear at this stage. Technical issues. Now, imagine you simply did what the manager suggested. Would you have solved the problem? Hardly. Moreover, the responsibility for the unresolved issue would fall on you, as the interface solution lies within the design domain. The product manager actually did their job correctly by identifying a problem: suspiciously, few users are clicking the button. Psychologically, taking on this bigger role isn’t easy. It means overcoming the fear of making mistakes and the discomfort of exploring unclear problems rather than just doing tasks. This shift means seeing ourselves as partners who create value — even if it means fighting a hesitation to question product managers— and understanding that using our product logic expertise proactively is crucial for modern designers. There’s another critical reason why we, designers, need to be a bit like product managers: the rise of AI. I deliberately used a simple example about enlarging a button, but I’m confident that in the near future, AI will easily handle routine design tasks. This worries me, but at the same time, I’m already gladly stepping into the product manager’s territory: understanding product and business metrics, formulating hypotheses, conducting research, and so on. It might sound like I’m taking work away from PMs, but believe me, they undoubtedly have enough on their plates and are usually more than happy to delegate some responsibilities to designers. Reason #3: You’re Solving The Wrong Problem Before solving anything, ask whether the problem even deserves your attention. During a major home‑screen redesign, our goal was to drive more users into paid services. The initial hypothesis — making service buttons bigger and brighter might help returning users — seemed reasonable enough to test. However, even when A/B testsshowed minimal impact, we continued to tweak those buttons. Only later did it click: the home screen isn’t the place to sell; visitors open the app to start, not to buy. We removed that promo block, and nothing broke. Contextual entry points deeper into the journey performed brilliantly. Lesson learned: Without the right context, any visual tweak is lipstick on a pig. Why did we get stuck polishing buttons instead of stopping sooner? It’s easy to get tunnel vision. Psychologically, it’s likely the good old sunk cost fallacy kicking in: we’d already invested time in the buttons, so stopping felt like wasting that effort, even though the data wasn’t promising. It’s just easier to keep fiddling with something familiar than to admit we need a new plan. Perhaps the simple question I should have asked myself when results stalled was: “Are we optimizing the right thing or just polishing something that fundamentally doesn’t fit the user’s primary goal here?” That alone might have saved hours. Reason #4: You’re Drowning In Unactionable Feedback We all discuss our work with colleagues. But here’s a crucial point: what kind of question do you pose to kick off that discussion? If your go-to is “What do you think?” well, that question might lead you down a rabbit hole of personal opinions rather than actionable insights. While experienced colleagues will cut through the noise, others, unsure what to evaluate, might comment on anything and everything — fonts, button colors, even when you desperately need to discuss a user flow. What matters here are two things: The question you ask, The context you give. That means clearly stating the problem, what you’ve learned, and how your idea aims to fix it. For instance: “The problem is our payment conversion rate has dropped by X%. I’ve interviewed users and found they abandon payment because they don’t understand how the total amount is calculated. My solution is to show a detailed cost breakdown. Do you think this actually solves the problem for them?” Here, you’ve stated the problem, shared your insight, explained your solution, and asked a direct question. It’s even better if you prepare a list of specific sub-questions. For instance: “Are all items in the cost breakdown clear?” or “Does the placement of this breakdown feel intuitive within the payment flow?” Another good habit is to keep your rough sketches and previous iterations handy. Some of your colleagues’ suggestions might be things you’ve already tried. It’s great if you can discuss them immediately to either revisit those ideas or definitively set them aside. I’m not a psychologist, but experience tells me that, psychologically, the reluctance to be this specific often stems from a fear of our solution being rejected. We tend to internalize feedback: a seemingly innocent comment like, “Have you considered other ways to organize this section?” or “Perhaps explore a different structure for this part?” can instantly morph in our minds into “You completely messed up the structure. You’re a bad designer.” Imposter syndrome, in all its glory. So, to wrap up this point, here are two recommendations: Prepare for every design discussion.A couple of focused questions will yield far more valuable input than a vague “So, what do you think?”. Actively work on separating feedback on your design from your self-worth.If a mistake is pointed out, acknowledge it, learn from it, and you’ll be less likely to repeat it. This is often easier said than done. For me, it took years of working with a psychotherapist. If you struggle with this, I sincerely wish you strength in overcoming it. Reason #5 You’re Just Tired Sometimes, the issue isn’t strategic at all — it’s fatigue. Fussing over icon corners can feel like a cozy bunker when your brain is fried. There’s a name for this: decision fatigue. Basically, your brain’s battery for hard thinking is low, so it hides out in the easy, comfy zone of pixel-pushing. A striking example comes from a New York Times article titled “Do You Suffer From Decision Fatigue?.” It described how judges deciding on release requests were far more likely to grant release early in the daycompared to late in the daysimply because their decision-making energy was depleted. Luckily, designers rarely hold someone’s freedom in their hands, but the example dramatically shows how fatigue can impact our judgment and productivity. What helps here: Swap tasks.Trade tickets with another designer; novelty resets your focus. Talk to another designer.If NDA permits, ask peers outside the team for a sanity check. Step away.Even a ten‑minute walk can do more than a double‑shot espresso. By the way, I came up with these ideas while walking around my office. I was lucky to work near a river, and those short walks quickly turned into a helpful habit. And one more trick that helps me snap out of detail mode early: if I catch myself making around 20 little tweaks — changing font weight, color, border radius — I just stop. Over time, it turned into a habit. I have a similar one with Instagram: by the third reel, my brain quietly asks, “Wait, weren’t we working?” Funny how that kind of nudge saves a ton of time. Four Steps I Use to Avoid Drowning In Detail Knowing these potential traps, here’s the practical process I use to stay on track: 1. Define the Core Problem & Business Goal Before anything, dig deep: what’s the actual problem we’re solving, not just the requested task or a surface-level symptom? Ask ‘why’ repeatedly. What user pain or business need are we addressing? Then, state the clear business goal: “What metric am I moving, and do we have data to prove this is the right lever?” If retention is the goal, decide whether push reminders, gamification, or personalised content is the best route. The wrong lever, or tackling a symptom instead of the cause, dooms everything downstream. 2. Choose the MechanicOnce the core problem and goal are clear, lock the solution principle or ‘mechanic’ first. Going with a game layer? Decide if it’s leaderboards, streaks, or badges. Write it down. Then move on. No UI yet. This keeps the focus high-level before diving into pixels. 3. Wireframe the Flow & Get Focused Feedback Now open Figma. Map screens, layout, and transitions. Boxes and arrows are enough. Keep the fidelity low so the discussion stays on the flow, not colour. Crucially, when you share these early wires, ask specific questions and provide clear contextto get actionable feedback, not just vague opinions. 4. Polish the VisualsI only let myself tweak grids, type scales, and shadows after the flow is validated. If progress stalls, or before a major polish effort, I surface the work in a design critique — again using targeted questions and clear context — instead of hiding in version 47. This ensures detailing serves the now-validated solution. Even for something as small as a single button, running these four checkpoints takes about ten minutes and saves hours of decorative dithering. Wrapping Up Next time you feel the pull to vanish into mock‑ups before the problem is nailed down, pause and ask what you might be avoiding. Yes, that can expose an uncomfortable truth. But pausing to ask what you might be avoiding — maybe the fuzzy core problem, or just asking for tough feedback — gives you the power to face the real issue head-on. It keeps the project focused on solving the right problem, not just perfecting a flawed solution. Attention to detail is a superpower when used at the right moment. Obsessing over pixels too soon, though, is a bad habit and a warning light telling us the process needs a rethink. #why #designers #get #stuck #details
    Why Designers Get Stuck In The Details And How To Stop
    smashingmagazine.com
    You’ve drawn fifty versions of the same screen — and you still hate every one of them. Begrudgingly, you pick three, show them to your product manager, and hear: “Looks cool, but the idea doesn’t work.” Sound familiar? In this article, I’ll unpack why designers fall into detail work at the wrong moment, examining both process pitfalls and the underlying psychological reasons, as understanding these traps is the first step to overcoming them. I’ll also share tactics I use to climb out of that trap. Reason #1 You’re Afraid To Show Rough Work We designers worship detail. We’re taught that true craft equals razor‑sharp typography, perfect grids, and pixel precision. So the minute a task arrives, we pop open Figma and start polishing long before polish is needed. I’ve skipped the sketch phase more times than I care to admit. I told myself it would be faster, yet I always ended up spending hours producing a tidy mock‑up when a scribbled thumbnail would have sparked a five‑minute chat with my product manager. Rough sketches felt “unprofessional,” so I hid them. The cost? Lost time, wasted energy — and, by the third redo, teammates were quietly wondering if I even understood the brief. The real problem here is the habit: we open Figma and start perfecting the UI before we’ve even solved the problem. So why do we hide these rough sketches? It’s not just a bad habit or plain silly. There are solid psychological reasons behind it. We often just call it perfectionism, but it’s deeper than wanting things neat. Digging into the psychology (like the research by Hewitt and Flett) shows there are a couple of flavors driving this: Socially prescribed perfectionismIt’s that nagging feeling that everyone else expects perfect work from you, which makes showing anything rough feel like walking into the lion’s den. Self-oriented perfectionismWhere you’re the one setting impossibly high standards for yourself, leading to brutal self-criticism if anything looks slightly off. Either way, the result’s the same: showing unfinished work feels wrong, and you miss out on that vital early feedback. Back to the design side, remember that clients rarely see architects’ first pencil sketches, but these sketches still exist; they guide structural choices before the 3D render. Treat your thumbnails the same way — artifacts meant to collapse uncertainty, not portfolio pieces. Once stakeholders see the upside, roughness becomes a badge of speed, not sloppiness. So, the key is to consciously make that shift: Treat early sketches as disposable tools for thinking and actively share them to get feedback faster. Reason #2: You Fix The Symptom, Not The Cause Before tackling any task, we need to understand what business outcome we’re aiming for. Product managers might come to us asking to enlarge the payment button in the shopping cart because users aren’t noticing it. The suggested solution itself isn’t necessarily bad, but before redesigning the button, we should ask, “What data suggests they aren’t noticing it?” Don’t get me wrong, I’m not saying you shouldn’t trust your product manager. On the contrary, these questions help ensure you’re on the same page and working with the same data. From my experience, here are several reasons why users might not be clicking that coveted button: Users don’t understand that this step is for payment. They understand it’s about payment but expect order confirmation first. Due to incorrect translation, users don’t understand what the button means. Lack of trust signals (no security icons, unclear seller information). Unexpected additional costs (hidden fees, shipping) that appear at this stage. Technical issues (inactive button, page freezing). Now, imagine you simply did what the manager suggested. Would you have solved the problem? Hardly. Moreover, the responsibility for the unresolved issue would fall on you, as the interface solution lies within the design domain. The product manager actually did their job correctly by identifying a problem: suspiciously, few users are clicking the button. Psychologically, taking on this bigger role isn’t easy. It means overcoming the fear of making mistakes and the discomfort of exploring unclear problems rather than just doing tasks. This shift means seeing ourselves as partners who create value — even if it means fighting a hesitation to question product managers (which might come from a fear of speaking up or a desire to avoid challenging authority) — and understanding that using our product logic expertise proactively is crucial for modern designers. There’s another critical reason why we, designers, need to be a bit like product managers: the rise of AI. I deliberately used a simple example about enlarging a button, but I’m confident that in the near future, AI will easily handle routine design tasks. This worries me, but at the same time, I’m already gladly stepping into the product manager’s territory: understanding product and business metrics, formulating hypotheses, conducting research, and so on. It might sound like I’m taking work away from PMs, but believe me, they undoubtedly have enough on their plates and are usually more than happy to delegate some responsibilities to designers. Reason #3: You’re Solving The Wrong Problem Before solving anything, ask whether the problem even deserves your attention. During a major home‑screen redesign, our goal was to drive more users into paid services. The initial hypothesis — making service buttons bigger and brighter might help returning users — seemed reasonable enough to test. However, even when A/B tests (a method of comparing two versions of a design to determine which performs better) showed minimal impact, we continued to tweak those buttons. Only later did it click: the home screen isn’t the place to sell; visitors open the app to start, not to buy. We removed that promo block, and nothing broke. Contextual entry points deeper into the journey performed brilliantly. Lesson learned: Without the right context, any visual tweak is lipstick on a pig. Why did we get stuck polishing buttons instead of stopping sooner? It’s easy to get tunnel vision. Psychologically, it’s likely the good old sunk cost fallacy kicking in: we’d already invested time in the buttons, so stopping felt like wasting that effort, even though the data wasn’t promising. It’s just easier to keep fiddling with something familiar than to admit we need a new plan. Perhaps the simple question I should have asked myself when results stalled was: “Are we optimizing the right thing or just polishing something that fundamentally doesn’t fit the user’s primary goal here?” That alone might have saved hours. Reason #4: You’re Drowning In Unactionable Feedback We all discuss our work with colleagues. But here’s a crucial point: what kind of question do you pose to kick off that discussion? If your go-to is “What do you think?” well, that question might lead you down a rabbit hole of personal opinions rather than actionable insights. While experienced colleagues will cut through the noise, others, unsure what to evaluate, might comment on anything and everything — fonts, button colors, even when you desperately need to discuss a user flow. What matters here are two things: The question you ask, The context you give. That means clearly stating the problem, what you’ve learned, and how your idea aims to fix it. For instance: “The problem is our payment conversion rate has dropped by X%. I’ve interviewed users and found they abandon payment because they don’t understand how the total amount is calculated. My solution is to show a detailed cost breakdown. Do you think this actually solves the problem for them?” Here, you’ve stated the problem (conversion drop), shared your insight (user confusion), explained your solution (cost breakdown), and asked a direct question. It’s even better if you prepare a list of specific sub-questions. For instance: “Are all items in the cost breakdown clear?” or “Does the placement of this breakdown feel intuitive within the payment flow?” Another good habit is to keep your rough sketches and previous iterations handy. Some of your colleagues’ suggestions might be things you’ve already tried. It’s great if you can discuss them immediately to either revisit those ideas or definitively set them aside. I’m not a psychologist, but experience tells me that, psychologically, the reluctance to be this specific often stems from a fear of our solution being rejected. We tend to internalize feedback: a seemingly innocent comment like, “Have you considered other ways to organize this section?” or “Perhaps explore a different structure for this part?” can instantly morph in our minds into “You completely messed up the structure. You’re a bad designer.” Imposter syndrome, in all its glory. So, to wrap up this point, here are two recommendations: Prepare for every design discussion.A couple of focused questions will yield far more valuable input than a vague “So, what do you think?”. Actively work on separating feedback on your design from your self-worth.If a mistake is pointed out, acknowledge it, learn from it, and you’ll be less likely to repeat it. This is often easier said than done. For me, it took years of working with a psychotherapist. If you struggle with this, I sincerely wish you strength in overcoming it. Reason #5 You’re Just Tired Sometimes, the issue isn’t strategic at all — it’s fatigue. Fussing over icon corners can feel like a cozy bunker when your brain is fried. There’s a name for this: decision fatigue. Basically, your brain’s battery for hard thinking is low, so it hides out in the easy, comfy zone of pixel-pushing. A striking example comes from a New York Times article titled “Do You Suffer From Decision Fatigue?.” It described how judges deciding on release requests were far more likely to grant release early in the day (about 70% of cases) compared to late in the day (less than 10%) simply because their decision-making energy was depleted. Luckily, designers rarely hold someone’s freedom in their hands, but the example dramatically shows how fatigue can impact our judgment and productivity. What helps here: Swap tasks.Trade tickets with another designer; novelty resets your focus. Talk to another designer.If NDA permits, ask peers outside the team for a sanity check. Step away.Even a ten‑minute walk can do more than a double‑shot espresso. By the way, I came up with these ideas while walking around my office. I was lucky to work near a river, and those short walks quickly turned into a helpful habit. And one more trick that helps me snap out of detail mode early: if I catch myself making around 20 little tweaks — changing font weight, color, border radius — I just stop. Over time, it turned into a habit. I have a similar one with Instagram: by the third reel, my brain quietly asks, “Wait, weren’t we working?” Funny how that kind of nudge saves a ton of time. Four Steps I Use to Avoid Drowning In Detail Knowing these potential traps, here’s the practical process I use to stay on track: 1. Define the Core Problem & Business Goal Before anything, dig deep: what’s the actual problem we’re solving, not just the requested task or a surface-level symptom? Ask ‘why’ repeatedly. What user pain or business need are we addressing? Then, state the clear business goal: “What metric am I moving, and do we have data to prove this is the right lever?” If retention is the goal, decide whether push reminders, gamification, or personalised content is the best route. The wrong lever, or tackling a symptom instead of the cause, dooms everything downstream. 2. Choose the Mechanic (Solution Principle) Once the core problem and goal are clear, lock the solution principle or ‘mechanic’ first. Going with a game layer? Decide if it’s leaderboards, streaks, or badges. Write it down. Then move on. No UI yet. This keeps the focus high-level before diving into pixels. 3. Wireframe the Flow & Get Focused Feedback Now open Figma. Map screens, layout, and transitions. Boxes and arrows are enough. Keep the fidelity low so the discussion stays on the flow, not colour. Crucially, when you share these early wires, ask specific questions and provide clear context (as discussed in ‘Reason #4’) to get actionable feedback, not just vague opinions. 4. Polish the Visuals (Mindfully) I only let myself tweak grids, type scales, and shadows after the flow is validated. If progress stalls, or before a major polish effort, I surface the work in a design critique — again using targeted questions and clear context — instead of hiding in version 47. This ensures detailing serves the now-validated solution. Even for something as small as a single button, running these four checkpoints takes about ten minutes and saves hours of decorative dithering. Wrapping Up Next time you feel the pull to vanish into mock‑ups before the problem is nailed down, pause and ask what you might be avoiding. Yes, that can expose an uncomfortable truth. But pausing to ask what you might be avoiding — maybe the fuzzy core problem, or just asking for tough feedback — gives you the power to face the real issue head-on. It keeps the project focused on solving the right problem, not just perfecting a flawed solution. Attention to detail is a superpower when used at the right moment. Obsessing over pixels too soon, though, is a bad habit and a warning light telling us the process needs a rethink.
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  • How To Create & Animate Breakdance-Inspired Streetwear

    IntroductionHi, my name is Pankaj Kholiya, and I am a Senior 3D Character Artist. I've been working in the game industry for the past 8 years. I worked on titles like Call of Duty: Black Ops 6, That Christmas, Ghost of Tsushima Director's Cut, Star Wars: Outlaws, Alan Wake 2, Street Fighter 6, and many more. Currently, I'm working as a freelancer for the gaming and cinematics industry.Since my last interview, I made a few personal works, was a part of a Netflix movie, That Christmas, and worked with Platige on Star Wars: Outlaws and Call of Duty: Black Ops 6 cinematic.The Breakdancing Clothing ProjectIt all started when I witnessed a dance battle that a friend organized. It was like watching Step Up live. There, I got the inspiration to create a break dancer. I started by gathering different references from the internet. I found one particular image on Pinterest and decided to recreate it in 3D.At first, the idea was to create the outfit in one pose, but along the way, I also decided to create a dancing version of the character and explore Unreal Engine. Here is the ref I used for the dancing version:Getting StartedFor the upcoming talents, I'll try to describe my process in a few points. Even before starting Marvelous Designer, I made sure to have my base character ready for animation and simulation. This time, I decided to use the MetaHuman creator for the base due to its high-quality textures and materials. My primary focus was on the clothing, so using MetaHuman saved a lot of time.After I was satisfied with how my MetaHuman looked, I took it to Mixamo to get some animations. I was really impressed by how good the animations worked on the MetaHuman. Once I had the animations, I took the animation into Marvelous Designer and simulated the clothes.For the posed character, I adjusted the rig to match the pose like the reference and used the same method as in this tutorial to pose the character:ClothingFor this particular project, I didn't focus on the topology as it was just for a single render. I just packed the UVs in Marvelous Designer, exported the quad mesh from Marvelous Designer, subdivided it a few times, and started working on the detailing part in ZBrush.For the texture, I used the low-division mesh from the ZBrush file, as I already had the UVs on it. I then baked the normal and other maps on it and took it to Substance 3D Painter.AnimationThere are multiple ways to animate the metahuman character. For this project, I've used Mixamo. I imported my character into Mixamo, selected the animation I liked, and exported it. After that, I just imported it to Marvelous Designer and hit the simulation button. You can check my previous breakdown for the Mixamo pipeline.Once happy with the result, I exported the simulated cloth as an Alembic to Unreal Engine. Tutorial for importing clothes into Unreal Engine:Lighting & RenderingThe main target was to match the lighting closely to the reference. This was my first project in Unreal Engine, so I wanted to explore the lighting and see how far I could go with it. Being new to the Unreal Engine, I went through a lot of tutorials. Here are the lights I've used for the posed version:For the dancing version, I've created a stage like the ref from the Step Up movie: Some tips I found useful for the rendering are in the video below:ConclusionAt first, I had a clear direction for this project and was confident in my skills to tackle the art aspect of it. But things changed when I dived into Unreal Engine for my presentation. More than half the time on this project went into learning and getting used to Unreal Engine. I don't regret a single second I invested in Unreal, as it was a new experience. It took around 15 days to wrap this one up.The lesson I learned is that upgrading your knowledge and learning new things will help you grow as an artist in the long run. Approaching how you make an artwork has changed a lot ever since I started 3D, and adapting to the changing art environment is a good thing. Here are some recommendations if you are interested in learning Unreal Engine.Pankaj Kholiya, Senior 3D Character ArtistInterview conducted by Amber Rutherford
    #how #create #ampamp #animate #breakdanceinspired
    How To Create & Animate Breakdance-Inspired Streetwear
    IntroductionHi, my name is Pankaj Kholiya, and I am a Senior 3D Character Artist. I've been working in the game industry for the past 8 years. I worked on titles like Call of Duty: Black Ops 6, That Christmas, Ghost of Tsushima Director's Cut, Star Wars: Outlaws, Alan Wake 2, Street Fighter 6, and many more. Currently, I'm working as a freelancer for the gaming and cinematics industry.Since my last interview, I made a few personal works, was a part of a Netflix movie, That Christmas, and worked with Platige on Star Wars: Outlaws and Call of Duty: Black Ops 6 cinematic.The Breakdancing Clothing ProjectIt all started when I witnessed a dance battle that a friend organized. It was like watching Step Up live. There, I got the inspiration to create a break dancer. I started by gathering different references from the internet. I found one particular image on Pinterest and decided to recreate it in 3D.At first, the idea was to create the outfit in one pose, but along the way, I also decided to create a dancing version of the character and explore Unreal Engine. Here is the ref I used for the dancing version:Getting StartedFor the upcoming talents, I'll try to describe my process in a few points. Even before starting Marvelous Designer, I made sure to have my base character ready for animation and simulation. This time, I decided to use the MetaHuman creator for the base due to its high-quality textures and materials. My primary focus was on the clothing, so using MetaHuman saved a lot of time.After I was satisfied with how my MetaHuman looked, I took it to Mixamo to get some animations. I was really impressed by how good the animations worked on the MetaHuman. Once I had the animations, I took the animation into Marvelous Designer and simulated the clothes.For the posed character, I adjusted the rig to match the pose like the reference and used the same method as in this tutorial to pose the character:ClothingFor this particular project, I didn't focus on the topology as it was just for a single render. I just packed the UVs in Marvelous Designer, exported the quad mesh from Marvelous Designer, subdivided it a few times, and started working on the detailing part in ZBrush.For the texture, I used the low-division mesh from the ZBrush file, as I already had the UVs on it. I then baked the normal and other maps on it and took it to Substance 3D Painter.AnimationThere are multiple ways to animate the metahuman character. For this project, I've used Mixamo. I imported my character into Mixamo, selected the animation I liked, and exported it. After that, I just imported it to Marvelous Designer and hit the simulation button. You can check my previous breakdown for the Mixamo pipeline.Once happy with the result, I exported the simulated cloth as an Alembic to Unreal Engine. Tutorial for importing clothes into Unreal Engine:Lighting & RenderingThe main target was to match the lighting closely to the reference. This was my first project in Unreal Engine, so I wanted to explore the lighting and see how far I could go with it. Being new to the Unreal Engine, I went through a lot of tutorials. Here are the lights I've used for the posed version:For the dancing version, I've created a stage like the ref from the Step Up movie: Some tips I found useful for the rendering are in the video below:ConclusionAt first, I had a clear direction for this project and was confident in my skills to tackle the art aspect of it. But things changed when I dived into Unreal Engine for my presentation. More than half the time on this project went into learning and getting used to Unreal Engine. I don't regret a single second I invested in Unreal, as it was a new experience. It took around 15 days to wrap this one up.The lesson I learned is that upgrading your knowledge and learning new things will help you grow as an artist in the long run. Approaching how you make an artwork has changed a lot ever since I started 3D, and adapting to the changing art environment is a good thing. Here are some recommendations if you are interested in learning Unreal Engine.Pankaj Kholiya, Senior 3D Character ArtistInterview conducted by Amber Rutherford #how #create #ampamp #animate #breakdanceinspired
    How To Create & Animate Breakdance-Inspired Streetwear
    80.lv
    IntroductionHi, my name is Pankaj Kholiya, and I am a Senior 3D Character Artist. I've been working in the game industry for the past 8 years. I worked on titles like Call of Duty: Black Ops 6, That Christmas, Ghost of Tsushima Director's Cut, Star Wars: Outlaws, Alan Wake 2, Street Fighter 6, and many more. Currently, I'm working as a freelancer for the gaming and cinematics industry.Since my last interview, I made a few personal works, was a part of a Netflix movie, That Christmas, and worked with Platige on Star Wars: Outlaws and Call of Duty: Black Ops 6 cinematic.The Breakdancing Clothing ProjectIt all started when I witnessed a dance battle that a friend organized. It was like watching Step Up live. There, I got the inspiration to create a break dancer. I started by gathering different references from the internet. I found one particular image on Pinterest and decided to recreate it in 3D.At first, the idea was to create the outfit in one pose, but along the way, I also decided to create a dancing version of the character and explore Unreal Engine. Here is the ref I used for the dancing version:Getting StartedFor the upcoming talents, I'll try to describe my process in a few points. Even before starting Marvelous Designer, I made sure to have my base character ready for animation and simulation. This time, I decided to use the MetaHuman creator for the base due to its high-quality textures and materials. My primary focus was on the clothing, so using MetaHuman saved a lot of time.After I was satisfied with how my MetaHuman looked, I took it to Mixamo to get some animations. I was really impressed by how good the animations worked on the MetaHuman. Once I had the animations, I took the animation into Marvelous Designer and simulated the clothes.For the posed character, I adjusted the rig to match the pose like the reference and used the same method as in this tutorial to pose the character:ClothingFor this particular project, I didn't focus on the topology as it was just for a single render. I just packed the UVs in Marvelous Designer, exported the quad mesh from Marvelous Designer, subdivided it a few times, and started working on the detailing part in ZBrush.For the texture, I used the low-division mesh from the ZBrush file, as I already had the UVs on it. I then baked the normal and other maps on it and took it to Substance 3D Painter.AnimationThere are multiple ways to animate the metahuman character. For this project, I've used Mixamo. I imported my character into Mixamo, selected the animation I liked, and exported it. After that, I just imported it to Marvelous Designer and hit the simulation button. You can check my previous breakdown for the Mixamo pipeline.Once happy with the result, I exported the simulated cloth as an Alembic to Unreal Engine. Tutorial for importing clothes into Unreal Engine:Lighting & RenderingThe main target was to match the lighting closely to the reference. This was my first project in Unreal Engine, so I wanted to explore the lighting and see how far I could go with it. Being new to the Unreal Engine, I went through a lot of tutorials. Here are the lights I've used for the posed version:For the dancing version, I've created a stage like the ref from the Step Up movie: Some tips I found useful for the rendering are in the video below:ConclusionAt first, I had a clear direction for this project and was confident in my skills to tackle the art aspect of it. But things changed when I dived into Unreal Engine for my presentation. More than half the time on this project went into learning and getting used to Unreal Engine. I don't regret a single second I invested in Unreal, as it was a new experience. It took around 15 days to wrap this one up.The lesson I learned is that upgrading your knowledge and learning new things will help you grow as an artist in the long run. Approaching how you make an artwork has changed a lot ever since I started 3D, and adapting to the changing art environment is a good thing. Here are some recommendations if you are interested in learning Unreal Engine.Pankaj Kholiya, Senior 3D Character ArtistInterview conducted by Amber Rutherford
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