Urban populations are expected to double by 2050, which means around 2.5 billion people could be added to urban areas by the middle of the century, driving the need for more sustainable urban planning and public services. Cities across the globe are turning to digital twins and AI agents for urban planning scenario analysis and data-driven operational decisions. Building a digital twin of a city and testing smart city AI agents within it, however, is a complex and resource-intensive endeavor, fraught with technical and operational challenges. To address those challenges, NVIDIA today announced the NVIDIA Omniverse Blueprint for smart city AI, a reference framework that combines the NVIDIA Omniverse, Cosmos, NeMo and Metropolis platforms to bring the benefits of physical AI to entire cities and their critical infrastructure. Using the blueprint, developers can build simulation-ready, or SimReady, photorealistic digital twins of cities to build and test AI agents that can help monitor and optimize city operations. Leading companies including XXII, AVES Reality, Akila, Blyncsy, Bentley, Cesium, K2K, Linker Vision, Milestone Systems, Nebius, SNCF Gares&Connexions, Trimble and Younite AI are among the first to use the new blueprint. NVIDIA Omniverse Blueprint for Smart City AI  The NVIDIA Omniverse Blueprint for smart city AI provides the complete software stack needed to accelerate the development and testing of AI agents in physically accurate digital twins of cities. It includes: NVIDIA Omniverse to build physically accurate digital twins and run simulations at city scale. NVIDIA Cosmos to generate synthetic data at scale for post-training AI models. NVIDIA NeMo to curate high-quality data and use that data to train and fine-tune vision language models (VLMs) and large language models. NVIDIA Metropolis to build and deploy video analytics AI agents based on the NVIDIA AI Blueprint for video search and summarization (VSS), helping process vast amounts of video data and provide critical insights to optimize business processes. The blueprint workflow comprises three key steps. First, developers create a SimReady digital twin of locations and facilities using aerial, satellite or map data with Omniverse and Cosmos. Second, they can train and fine-tune AI models, like computer vision models and VLMs, using NVIDIA TAO and NeMo Curator to improve accuracy for vision AI use cases​. Finally, real-time AI agents powered by these customized models are deployed to alert, summarize and query camera and sensor data using the Metropolis VSS blueprint. NVIDIA Partner Ecosystem Powers Smart Cities Worldwide The blueprint for smart city AI enables a large ecosystem of partners to use a single workflow to build and activate digital twins for smart city use cases, tapping into a combination of NVIDIA’s technologies and their own. SNCF Gares&Connexions, which operates a network of 3,000 train stations across France and Monaco, has deployed a digital twin and AI agents to enable real-time operational monitoring, emergency response simulations and infrastructure upgrade planning. This helps each station analyze operational data such as energy and water use, and enables predictive maintenance capabilities, automated reporting and GDPR-compliant video analytics for incident detection and crowd management. Powered by Omniverse, Metropolis and solutions from ecosystem partners Akila and XXII, SNCF Gares&Connexions’ physical AI deployment at the Monaco-Monte-Carlo and Marseille stations has helped SNCF Gares&Connexions achieve a 100% on-time preventive maintenance completion rate, a 50% reduction in downtime and issue response time, and a 20% reduction in energy consumption. https://blogs.nvidia.com/wp-content/uploads/2025/06/01-Monaco-Akila.mp4 The city of Palermo in Sicily is using AI agents and digital twins from its partner K2K to improve public health and safety by helping city operators process and analyze footage from over 1,000 public video streams at a rate of nearly 50 billion pixels per second. Tapped by Sicily, K2K’s AI agents — built with the NVIDIA AI Blueprint for VSS and cloud solutions from Nebius — can interpret and act on video data to provide real-time alerts on public events. To accurately predict and resolve traffic incidents, K2K is generating synthetic data with Cosmos world foundation models to simulate different driving conditions. Then, K2K uses the data to fine-tune the VLMs powering the AI agents with NeMo Curator. These simulations enable K2K’s AI agents to create over 100,000 predictions per second. https://blogs.nvidia.com/wp-content/uploads/2025/06/02-K2K-Polermo-1600x900-1.mp4 Milestone Systems — in collaboration with NVIDIA and European cities — has launched Project Hafnia, an initiative to build an anonymized, ethically sourced video data platform for cities to develop and train AI models and applications while maintaining regulatory compliance. Using a combination of Cosmos and NeMo Curator on NVIDIA DGX Cloud and Nebius’ sovereign European cloud infrastructure, Project Hafnia scales up and enables European-compliant training and fine-tuning of video-centric AI models, including VLMs, for a variety of smart city use cases. The project’s initial rollout, taking place in Genoa, Italy, features one of the world’s first VLM models for intelligent transportation systems. https://blogs.nvidia.com/wp-content/uploads/2025/06/03-Milestone.mp4 Linker Vision was among the first to partner with NVIDIA to deploy smart city digital twins and AI agents for Kaohsiung City, Taiwan — powered by Omniverse, Cosmos and Metropolis. Linker Vision worked with AVES Reality, a digital twin company, to bring aerial imagery of cities and infrastructure into 3D geometry and ultimately into SimReady Omniverse digital twins. Linker Vision’s AI-powered application then built, trained and tested visual AI agents in a digital twin before deployment in the physical city. Now, it’s scaling to analyze 50,000 video streams in real time with generative AI to understand and narrate complex urban events like floods and traffic accidents. Linker Vision delivers timely insights to a dozen city departments through a single integrated AI-powered platform, breaking silos and reducing incident response times by up to 80%. https://blogs.nvidia.com/wp-content/uploads/2025/06/02-Linker-Vision-1280x680-1.mp4 Bentley Systems is joining the effort to bring physical AI to cities with the NVIDIA blueprint. Cesium, the open 3D geospatial platform, provides the foundation for visualizing, analyzing and managing infrastructure projects and ports digital twins to Omniverse. The company’s AI platform Blyncsy uses synthetic data generation and Metropolis to analyze road conditions and improve maintenance. Trimble, a global technology company that enables essential industries including construction, geospatial and transportation, is exploring ways to integrate components of the Omniverse blueprint into its reality capture workflows and Trimble Connect digital twin platform for surveying and mapping applications for smart cities. Younite AI, a developer of AI and 3D digital twin solutions, is adopting the blueprint to accelerate its development pipeline, enabling the company to quickly move from operational digital twins to large-scale urban simulations, improve synthetic data generation, integrate real-time IoT sensor data and deploy AI agents. Learn more about the NVIDIA Omniverse Blueprint for smart city AI by attending this GTC Paris session or watching the on-demand video after the event. Sign up to be notified when the blueprint is available. Watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang at VivaTech, and explore GTC Paris sessions.

As a global labor shortage leaves 50 million positions unfilled across industries like manufacturing and logistics, Hexagon — a global leader in measurement technologies — is developing humanoid robots that can lend a helping hand. Industrial sectors depend on skilled workers to perform a variety of error-prone tasks, including operating high-precision scanners for reality capture — the process of capturing digital data to replicate the real world in simulation. At the Hexagon LIVE Global conference, Hexagon’s robotics division today unveiled AEON — a new humanoid robot built in collaboration with NVIDIA that’s engineered to perform a wide range of industrial applications, from manipulation and asset inspection to reality capture and operator support. Hexagon plans to deploy AEON across automotive, transportation, aerospace, manufacturing, warehousing and logistics. Future use cases for AEON include: Reality capture, which involves automatic planning and then scanning of assets, industrial spaces and environments to generate 3D models. The captured data is then used for advanced visualization and collaboration in the Hexagon Digital Reality (HxDR) platform powering Hexagon Reality Cloud Studio (RCS). Manipulation tasks, such as sorting and moving parts in various industrial and manufacturing settings. Part inspection, which includes checking parts for defects or ensuring adherence to specifications. Industrial operations, including highly dexterous technical tasks like machinery operations, teleoperation and scanning parts using high-end scanners. “The age of general-purpose robotics has arrived, due to technological advances in simulation and physical AI,” said Deepu Talla, vice president of robotics and edge AI at NVIDIA. “Hexagon’s new AEON humanoid embodies the integration of NVIDIA’s three-computer robotics platform and is making a significant leap forward in addressing industry-critical challenges.” Using NVIDIA’s Three Computers to Develop AEON  To build AEON, Hexagon used NVIDIA’s three computers for developing and deploying physical AI systems. They include AI supercomputers to train and fine-tune powerful foundation models; the NVIDIA Omniverse platform, running on NVIDIA OVX servers, for testing and optimizing these models in simulation environments using real and physically based synthetic data; and NVIDIA IGX Thor robotic computers to run the models. Hexagon is exploring using NVIDIA accelerated computing to post-train the NVIDIA Isaac GR00T N1.5 open foundation model to improve robot reasoning and policies, and tapping Isaac GR00T-Mimic to generate vast amounts of synthetic motion data from a few human demonstrations. AEON learns many of its skills through simulations powered by the NVIDIA Isaac platform. Hexagon uses NVIDIA Isaac Sim, a reference robotic simulation application built on Omniverse, to simulate complex robot actions like navigation, locomotion and manipulation. These skills are then refined using reinforcement learning in NVIDIA Isaac Lab, an open-source framework for robot learning. https://blogs.nvidia.com/wp-content/uploads/2025/06/Copy-of-robotics-hxgn-live-blog-1920x1080-1.mp4 This simulation-first approach enabled Hexagon to fast-track its robotic development, allowing AEON to master core locomotion skills in just 2-3 weeks — rather than 5-6 months — before real-world deployment. In addition, AEON taps into NVIDIA Jetson Orin onboard computers to autonomously move, navigate and perform its tasks in real time, enhancing its speed and accuracy while operating in complex and dynamic environments. Hexagon is also planning to upgrade AEON with NVIDIA IGX Thor to enable functional safety for collaborative operation. “Our goal with AEON was to design an intelligent, autonomous humanoid that addresses the real-world challenges industrial leaders have shared with us over the past months,” said Arnaud Robert, president of Hexagon’s robotics division. “By leveraging NVIDIA’s full-stack robotics and simulation platforms, we were able to deliver a best-in-class humanoid that combines advanced mechatronics, multimodal sensor fusion and real-time AI.” Data Comes to Life Through Reality Capture and Omniverse Integration  AEON will be piloted in factories and warehouses to scan everything from small precision parts and automotive components to large assembly lines and storage areas. Captured data comes to life in RCS, a platform that allows users to collaborate, visualize and share reality-capture data by tapping into HxDR and NVIDIA Omniverse running in the cloud. This removes the constraint of local infrastructure. “Digital twins offer clear advantages, but adoption has been challenging in several industries,” said Lucas Heinzle, vice president of research and development at Hexagon’s robotics division. “AEON’s sophisticated sensor suite enables the integration of reality data capture with NVIDIA Omniverse, streamlining workflows for our customers and moving us closer to making digital twins a mainstream tool for collaboration and innovation.” AEON’s Next Steps By adopting the OpenUSD framework and developing on Omniverse, Hexagon can generate high-fidelity digital twins from scanned data — establishing a data flywheel to continuously train AEON. This latest work with Hexagon is helping shape the future of physical AI — delivering scalable, efficient solutions to address the challenges faced by industries that depend on capturing real-world data. Watch the Hexagon LIVE keynote, explore presentations and read more about AEON. All imagery courtesy of Hexagon.

Editor’s note: This blog is a part of Into the Omniverse, a series focused on how developers, 3D practitioners and enterprises can transform their workflows using the latest advances in OpenUSD and NVIDIA Omniverse. Simulated driving environments enable engineers to safely and efficiently train, test and validate autonomous vehicles (AVs) across countless real-world and edge-case scenarios without the risks and costs of physical testing. These simulated environments can be created through neural reconstruction of real-world data from AV fleets or generated with world foundation models (WFMs) — neural networks that understand physics and real-world properties. WFMs can be used to generate synthetic datasets for enhanced AV simulation. To help physical AI developers build such simulated environments, NVIDIA unveiled major advances in WFMs at the GTC Paris and CVPR conferences earlier this month. These new capabilities enhance NVIDIA Cosmos — a platform of generative WFMs, advanced tokenizers, guardrails and accelerated data processing tools. Key innovations like Cosmos Predict-2, the Cosmos Transfer-1 NVIDIA preview NIM microservice and Cosmos Reason are improving how AV developers generate synthetic data, build realistic simulated environments and validate safety systems at unprecedented scale. Universal Scene Description (OpenUSD), a unified data framework and standard for physical AI applications, enables seamless integration and interoperability of simulation assets across the development pipeline. OpenUSD standardization plays a critical role in ensuring 3D pipelines are built to scale. NVIDIA Omniverse, a platform of application programming interfaces, software development kits and services for building OpenUSD-based physical AI applications, enables simulations from WFMs and neural reconstruction at world scale. Leading AV organizations — including Foretellix, Mcity, Oxa, Parallel Domain, Plus AI and Uber — are among the first to adopt Cosmos models. Foundations for Scalable, Realistic Simulation Cosmos Predict-2, NVIDIA’s latest WFM, generates high-quality synthetic data by predicting future world states from multimodal inputs like text, images and video. This capability is critical for creating temporally consistent, realistic scenarios that accelerate training and validation of AVs and robots. In addition, Cosmos Transfer, a control model that adds variations in weather, lighting and terrain to existing scenarios, will soon be available to 150,000 developers on CARLA, a leading open-source AV simulator. This greatly expands the broad AV developer community’s access to advanced AI-powered simulation tools. Developers can start integrating synthetic data into their own pipelines using the NVIDIA Physical AI Dataset. The latest release includes 40,000 clips generated using Cosmos. Building on these foundations, the Omniverse Blueprint for AV simulation provides a standardized, API-driven workflow for constructing rich digital twins, replaying real-world sensor data and generating new ground-truth data for closed-loop testing. The blueprint taps into OpenUSD’s layer-stacking and composition arcs, which enable developers to collaborate asynchronously and modify scenes nondestructively. This helps create modular, reusable scenario variants to efficiently generate different weather conditions, traffic patterns and edge cases. Driving the Future of AV Safety To bolster the operational safety of AV systems, NVIDIA earlier this year introduced NVIDIA Halos — a comprehensive safety platform that integrates the company’s full automotive hardware and software stack with AI research focused on AV safety. The new Cosmos models — Cosmos Predict- 2, Cosmos Transfer- 1 NIM and Cosmos Reason — deliver further safety enhancements to the Halos platform, enabling developers to create diverse, controllable and realistic scenarios for training and validating AV systems. These models, trained on massive multimodal datasets including driving data, amplify the breadth and depth of simulation, allowing for robust scenario coverage — including rare and safety-critical events — while supporting post-training customization for specialized AV tasks. At CVPR, NVIDIA was recognized as an Autonomous Grand Challenge winner, highlighting its leadership in advancing end-to-end AV workflows. The challenge used OpenUSD’s robust metadata and interoperability to simulate sensor inputs and vehicle trajectories in semi-reactive environments, achieving state-of-the-art results in safety and compliance. Learn more about how developers are leveraging tools like CARLA, Cosmos, and Omniverse to advance AV simulation in this livestream replay: Hear NVIDIA Director of Autonomous Vehicle Research Marco Pavone on the NVIDIA AI Podcast share how digital twins and high-fidelity simulation are improving vehicle testing, accelerating development and reducing real-world risks. Get Plugged Into the World of OpenUSD Learn more about what’s next for AV simulation with OpenUSD by watching the replay of NVIDIA founder and CEO Jensen Huang’s GTC Paris keynote. Looking for more live opportunities to learn more about OpenUSD? Don’t miss sessions and labs happening at SIGGRAPH 2025, August 10–14. Discover why developers and 3D practitioners are using OpenUSD and learn how to optimize 3D workflows with the self-paced “Learn OpenUSD” curriculum for 3D developers and practitioners, available for free through the NVIDIA Deep Learning Institute. Explore the Alliance for OpenUSD forum and the AOUSD website. Stay up to date by subscribing to NVIDIA Omniverse news, joining the community and following NVIDIA Omniverse on Instagram, LinkedIn, Medium and X.

In April 2025, at the ITER Private Sector Fusion Workshop in Cadarache, something remarkable unfolded. In a room filled with scientists, engineers and software visionaries, the line between big science and commercial innovation began to blur.   Three organisations – Microsoft Research, Arena and Brigantium Engineering – shared how artificial intelligence (AI), already transforming everything from language models to logistics, is now stepping into a new role: helping humanity to unlock the power of nuclear fusion.  Each presenter addressed a different part of the puzzle, but the message was the same: AI isn’t just a buzzword anymore. It’s becoming a real tool – practical, powerful and indispensable – for big science and engineering projects, including fusion.  “If we think of the agricultural revolution and the industrial revolution, the AI revolution is next – and it’s coming at a pace which is unprecedented,” said Kenji Takeda, director of research incubations at Microsoft Research.  Microsoft’s collaboration with ITER is already in motion. Just a month before the workshop, the two teams signed a Memorandum of Understanding (MoU) to explore how AI can accelerate research and development. This follows ITER’s initial use of Microsoft technology to empower their teams. A chatbot in Azure OpenAI service was developed to help staff navigate technical knowledge, on more than a million ITER documents, using natural conversation. GitHub Copilot assists with coding, while AI helps to resolve IT support tickets – those everyday but essential tasks that keep the lights on.  But Microsoft’s vision goes deeper. Fusion demands materials that can survive extreme conditions – heat, radiation, pressure – and that’s where AI shows a different kind of potential. MatterGen, a Microsoft Research generative AI model for materials, designs entirely new materials based on specific properties. “It’s like ChatGPT,” said Takeda, “but instead of ‘Write me a poem’, we ask it to design a material that can survive as the first wall of a fusion reactor.”  The next step? MatterSim – a simulation tool that predicts how these imagined materials will behave in the real world. By combining generation and simulation, Microsoft hopes to uncover materials that don’t yet exist in any catalogue.  While Microsoft tackles the atomic scale, Arena is focused on a different challenge: speeding up hardware development. As general manager Michael Frei put it: “Software innovation happens in seconds. In hardware, that loop can take months – or years.”  Arena’s answer is Atlas, a multimodal AI platform that acts as an extra set of hands – and eyes – for engineers. It can read data sheets, interpret lab results, analyse circuit diagrams and even interact with lab equipment through software interfaces. “Instead of adjusting an oscilloscope manually,” said Frei, “you can just say, ‘Verify the I2C [inter integrated circuit] protocol’, and Atlas gets it done.”  It doesn’t stop there. Atlas can write and adapt firmware on the fly, responding to real-time conditions. That means tighter feedback loops, faster prototyping and fewer late nights in the lab. Arena aims to make building hardware feel a little more like writing software – fluid, fast and assisted by smart tools.  Fusion, of course, isn’t just about atoms and code – it’s also about construction. Gigantic, one-of-a-kind machines don’t build themselves. That’s where Brigantium Engineering comes in. Founder Lynton Sutton explained how his team uses “4D planning” – a marriage of 3D CAD models and detailed construction schedules – to visualise how everything comes together over time. “Gantt charts are hard to interpret. 3D models are static. Our job is to bring those together,” he said.  The result is a time-lapse-style animation that shows the construction process step by step. It’s proven invaluable for safety reviews and stakeholder meetings. Rather than poring over spreadsheets, teams can simply watch the plan come to life.  And there’s more. Brigantium is bringing these models into virtual reality using Unreal Engine – the same one behind many video games. One recent model recreated ITER’s tokamak pit using drone footage and photogrammetry. The experience is fully interactive and can even run in a web browser. “We’ve really improved the quality of the visualisation,” said Sutton. “It’s a lot smoother; the textures look a lot better. Eventually, we’ll have this running through a web browser, so anybody on the team can just click on a web link to navigate this 4D model.”  Looking forward, Sutton believes AI could help automate the painstaking work of syncing schedules with 3D models. One day, these simulations could reach all the way down to individual bolts and fasteners – not just with impressive visuals, but with critical tools for preventing delays.  Despite the different approaches, one theme ran through all three presentations: AI isn’t just a tool for office productivity. It’s becoming a partner in creativity, problem-solving and even scientific discovery.  Takeda mentioned that Microsoft is experimenting with “world models” inspired by how video games simulate physics. These models learn about the physical world by watching pixels in the form of videos of real phenomena such as plasma behaviour. “Our thesis is that if you showed this AI videos of plasma, it might learn the physics of plasmas,” he said.  It sounds futuristic, but the logic holds. The more AI can learn from the world, the more it can help us understand it – and perhaps even master it. At its heart, the message from the workshop was simple: AI isn’t here to replace the scientist, the engineer or the planner; it’s here to help, and to make their work faster, more flexible and maybe a little more fun. As Takeda put it: “Those are just a few examples of how AI is starting to be used at ITER. And it’s just the start of that journey.”  If these early steps are any indication, that journey won’t just be faster – it might also be more inspired. 

Unity Technical VFX ArtistNo Brakes GamesVilnius, Lithuania or Remote3 hours agoApplyWe are No Brakes Games, the creators of Human Fall Flat.We are looking for a Unity Technical VFX Artist to join our team to work on Human Fall Flat 2.FULL-TIME POSITION - Vilnius, Lithuania or RemoteRole Overview:As a Unity Technical Artist specializing in VFX and rendering, you will develop and optimize real-time visual effects, create advanced shaders and materials, and ensure a balance between visual fidelity and performance. You will solve complex technical challenges, optimizing effects for real-time execution while collaborating with artists, designers, and engineers to push Unity’s rendering capabilities to the next level.Responsibilities:Develop and optimize real-time VFX solutions that are both visually striking and performant.Create scalable shaders and materials for water, fog, atmospheric effects, and dynamic lighting.Debug and resolve VFX performance bottlenecks using Unity Profiler, RenderDoc, and other tools.Optimize particle systems, volumetric effects, and GPU simulations for multi-platform performance.Document best practices and educate the team on efficient asset and shader workflows.Collaborate with engineers to develop and implement custom rendering solutions.Stay updated on Unity’s latest advancements in rendering, HDRP, and the Visual Effect Graph.Requirements:2+ years of experience as a Technical Artist in game development, with a focus on Unity.Strong understanding of Unity’s rendering pipeline (HDRP, URP, Built-in) and shader development (HLSL, Shader Graph).Experience developing performance-conscious visual effects, including particle systems, volumetric lighting, and dynamic environmental effects.Proficiency in GPU/CPU optimization techniques, LODs for VFX.Hands-on experience with real-time lighting and atmospheric effects.Ability to debug and profile complex rendering issues effectively.Excellent communication skills and ability to work collaboratively within a multi-disciplinary team.A flexible, R&D-driven mindset, able to iterate quickly in both prototyping and production environments.Nice-to-Have:Experience working on at least one released game project.Experience with Unity HDRP and SRP.Experience with multi-platform development (PC, console, mobile, VR/AR).Knowledge of C#, Python, or C++ for extending Unity’s capabilities.Experience developing custom node-based tools or extending Unity’s Visual Effect Graph.Background in procedural animation, physics-based effects, or fluid simulations.Apply today by sending your Portfolio & CV to jobs@nobrakesgames.com Create Your Profile — Game companies can contact you with their relevant job openings. Apply

We are pleased to announce the release of Matali Physics 6.9, the next significant step on the way to the seventh major version of the environment. Matali Physics 6.9 introduces a number of improvements and fixes to Matali Physics Core, Matali Render and Matali Games modules, presents physics-driven, completely dynamic light sources, real-time object scaling with destruction, lighting model simulating global illumination (GI) in some aspects, comprehensive support for Wayland on Linux, and more. Posted by komires on Jun 3rd, 2025 What is Matali Physics? Matali Physics is an advanced, modern, multi-platform, high-performance 3d physics environment intended for games, VR, AR, physics-based simulations and robotics. Matali Physics consists of the advanced 3d physics engine Matali Physics Core and other physics-driven modules that all together provide comprehensive simulation of physical phenomena and physics-based modeling of both real and imaginary objects. What's new in version 6.9? Physics-driven, completely dynamic light sources. The introduced solution allows for processing hundreds of movable, long-range and shadow-casting light sources, where with each source can be assigned logic that controls its behavior, changes light parameters, volumetric effects parameters and others; Real-time object scaling with destruction. All groups of physics objects and groups of physics objects with constraints may be subject to destruction process during real-time scaling, allowing group members to break off at different sizes; Lighting model simulating global illumination (GI) in some aspects. Based on own research and development work, processed in real time, ready for dynamic scenes, fast on mobile devices, not based on lightmaps, light probes, baked lights, etc.; Comprehensive support for Wayland on Linux. The latest version allows Matali Physics SDK users to create advanced, high-performance, physics-based, Vulkan-based games for modern Linux distributions where Wayland is the main display server protocol; Other improvements and fixes which complete list is available on the History webpage. What platforms does Matali Physics support? Android Android TV *BSD iOS iPadOS Linux (distributions) macOS Steam Deck tvOS UWP (Desktop, Xbox Series X/S) Windows (Classic, GDK, Handheld consoles) What are the benefits of using Matali Physics? Physics simulation, graphics, sound and music integrated into one total multimedia solution where creating complex interactions and behaviors is common and relatively easy Composed of dedicated modules that do not require additional licences and fees Supports fully dynamic and destructible scenes Supports physics-based behavioral animations Supports physical AI, object motion and state change control Supports physics-based GUI Supports physics-based particle effects Supports multi-scene physics simulation and scene combining Supports physics-based photo mode Supports physics-driven sound Supports physics-driven music Supports debug visualization Fully serializable and deserializable Available for all major mobile, desktop and TV platforms New features on request Dedicated technical support Regular updates and fixes If you have questions related to the latest version and the use of Matali Physics environment as a game creation solution, please do not hesitate to contact us.

VFX ArtistNo Brakes GamesVilnius, Lithuania or Remote2 hours agoApplyWe are No Brakes Games, the creators of Human Fall Flat.We are now looking for an VFX Artist to join our team to work on Human Fall Flat 2.FULL-TIME POSITIONRole Overview:As a VFX Artist, you will develop and optimize real-time visual effects and ensure a balance between visual fidelity and performance.Responsibilities:Develop and optimize real-time VFX solutions that are both visually striking and performant.Debug and resolve VFX performance bottlenecks using Unity Profiler, RenderDoc, and other tools.Optimize particle systems, volumetric effects, and GPU simulations for multi-platform performance.Stay updated on Unity’s latest advancements in rendering, HDRP, and the Visual Effect Graph.Requirements:2+ years of experience as a VFX Artist in game development, with a focus on Unity.Experience developing performance-conscious visual effects, including particle systems, volumetric lighting, and dynamic environmental effects.Proficiency in GPU/CPU optimization techniques, LODs for VFX.Excellent communication skills and ability to work collaboratively within a multi-disciplinary team.A flexible, R&D-driven mindset, able to iterate quickly in both prototyping and production environments.Nice-to-Have:Experience working on at least one released game project.Experience with Unity HDRP and SRP.Experience with multi-platform development (PC, console, mobile, VR/AR).Experience developing custom node-based tools or extending Unity’s Visual Effect Graph.Background in procedural animation, physics-based effects, or fluid simulations.Apply today by sending your Portfolio & CV to jobs@nobrakesgames.com Create Your Profile — Game companies can contact you with their relevant job openings. Apply

If you’ve ever flown through outer space, at least while watching a documentary or a science fiction film, you’ve seen how artists turn astronomical findings into stunning visuals. But in the process of visualizing data for their latest planetarium show, a production team at New York’s American Museum of Natural History made a surprising discovery of their own: a trillion-and-a-half mile long spiral of material drifting along the edge of our solar system. “So this is a really fun thing that happened,” says Jackie Faherty, the museum’s senior scientist. Last winter, Faherty and her colleagues were beneath the dome of the museum’s Hayden Planetarium, fine-tuning a scene that featured the Oort cloud, the big, thick bubble surrounding our Sun and planets that’s filled with ice and rock and other remnants from the solar system’s infancy. The Oort cloud begins far beyond Neptune, around one and a half light years from the Sun. It has never been directly observed; its existence is inferred from the behavior of long-period comets entering the inner solar system. The cloud is so expansive that the Voyager spacecraft, our most distant probes, would need another 250 years just to reach its inner boundary; to reach the other side, they would need about 30,000 years.  The 30-minute show, Encounters in the Milky Way, narrated by Pedro Pascal, guides audiences on a trip through the galaxy across billions of years. For a section about our nascent solar system, the writing team decided “there’s going to be a fly-by” of the Oort cloud, Faherty says. “But what does our Oort cloud look like?”  To find out, the museum consulted astronomers and turned to David Nesvorný, a scientist at the Southwest Research Institute in San Antonio. He provided his model of the millions of particles believed to make up the Oort cloud, based on extensive observational data. “Everybody said, go talk to Nesvorný. He’s got the best model,” says Faherty. And “everybody told us, ‘There’s structure in the model,’ so we were kind of set up to look for stuff,” she says.  The museum’s technical team began using Nesvorný’s model to simulate how the cloud evolved over time. Later, as the team projected versions of the fly-by scene into the dome, with the camera looking back at the Oort cloud, they saw a familiar shape, one that appears in galaxies, Saturn’s rings, and disks around young stars. “We’re flying away from the Oort cloud and out pops this spiral, a spiral shape to the outside of our solar system,” Faherty marveled. “A huge structure, millions and millions of particles.” She emailed Nesvorný to ask for “more particles,” with a render of the scene attached. “We noticed the spiral of course,” she wrote. “And then he writes me back: ‘what are you talking about, a spiral?’”  While fine-tuning a simulation of the Oort cloud, a vast expanse of ice material leftover from the birth of our Sun, the ‘Encounters in the Milky Way’ production team noticed a very clear shape: a structure made of billions of comets and shaped like a spiral-armed galaxy, seen here in a scene from the final Space Show (curving, dusty S-shape behind the Sun) [Image: © AMNH] More simulations ensued, this time on Pleiades, a powerful NASA supercomputer. In high-performance computer simulations spanning 4.6 billion years, starting from the Solar System’s earliest days, the researchers visualized how the initial icy and rocky ingredients of the Oort cloud began circling the Sun, in the elliptical orbits that are thought to give the cloud its rough disc shape. The simulations also incorporated the physics of the Sun’s gravitational pull, the influences from our Milky Way galaxy, and the movements of the comets themselves.  In each simulation, the spiral persisted. “No one has ever seen the Oort structure like that before,” says Faherty. Nesvorný “has a great quote about this: ‘The math was all there. We just needed the visuals.’”  An illustration of the Kuiper Belt and Oort Cloud in relation to our solar system. [Image: NASA] As the Oort cloud grew with the early solar system, Nesvorný and his colleagues hypothesize that the galactic tide, or the gravitational force from the Milky Way, disrupted the orbits of some comets. Although the Sun pulls these objects inward, the galaxy’s gravity appears to have twisted part of the Oort cloud outward, forming a spiral tilted roughly 30 degrees from the plane of the solar system. “As the galactic tide acts to decouple bodies from the scattered disk it creates a spiral structure in physical space that is roughly 15,000 astronomical units in length,” or around 1.4 trillion miles from one end to the other, the researchers write in a paper that was published in March in the Astrophysical Journal. “The spiral is long-lived and persists in the inner Oort Cloud to the present time.” “The physics makes sense,” says Faherty. “Scientists, we’re amazing at what we do, but it doesn’t mean we can see everything right away.” It helped that the team behind the space show was primed to look for something, says Carter Emmart, the museum’s director of astrovisualization and director of Encounters. Astronomers had described Nesvorný’s model as having “a structure,” which intrigued the team’s artists. “We were also looking for structure so that it wouldn’t just be sort of like a big blob,” he says. “Other models were also revealing this—but they just hadn’t been visualized.” The museum’s attempts to simulate nature date back to its first habitat dioramas in the early 1900s, which brought visitors to places that hadn’t yet been captured by color photos, TV, or the web. The planetarium, a night sky simulator for generations of would-be scientists and astronauts, got its start after financier Charles Hayden bought the museum its first Zeiss projector. The planetarium now boasts one of the world’s few Zeiss Mark IX systems. Still, these days the star projector is rarely used, Emmart says, now that fulldome laser projectors can turn the old static starfield into 3D video running at 60 frames per second. The Hayden boasts six custom-built Christie projectors, part of what the museum’s former president called “the most advanced planetarium ever attempted.”  In about 1.3 million years, the star system Gliese 710 is set to pass directly through our Oort Cloud, an event visualized in a dramatic scene in ‘Encounters in the Milky Way.’ During its flyby, our systems will swap icy comets, flinging some out on new paths. [Image: © AMNH] Emmart recalls how in 1998, when he and other museum leaders were imagining the future of space shows at the Hayden—now with the help of digital projectors and computer graphics—there were questions over how much space they could try to show. “We’re talking about these astronomical data sets we could plot to make the galaxy and the stars,” he says. “Of course, we knew that we would have this star projector, but we really wanted to emphasize astrophysics with this dome video system. I was drawing pictures of this just to get our heads around it and noting the tip of the solar system to the Milky Way is about 60 degrees. And I said, what are we gonna do when we get outside the Milky Way?’ “Then [planetarium’s director] Neil Degrasse Tyson “goes, ‘whoa, whoa, whoa, Carter, we have enough to do. And just plotting the Milky Way, that’s hard enough.’ And I said, ‘well, when we exit the Milky Way and we don’t see any other galaxies, that’s sort of like astronomy in 1920—we thought maybe the entire universe is just a Milky Way.'” “And that kind of led to a chaotic discussion about, well, what other data sets are there for this?” Emmart adds. The museum worked with astronomer Brent Tully, who had mapped 3500 galaxies beyond the Milky Way, in collaboration with the National Center for Super Computing Applications. “That was it,” he says, “and that seemed fantastical.” By the time the first planetarium show opened at the museum’s new Rose Center for Earth and Space in 2000, Tully had broadened his survey “to an amazing” 30,000 galaxies. The Sloan Digital Sky Survey followed—it’s now at data release 18—with six million galaxies. To build the map of the universe that underlies Encounters, the team also relied on data from the European Space Agency’s space observatory, Gaia. Launched in 2013 and powered down in March of this year, Gaia brought an unprecedented precision to our astronomical map, plotting the distance between 1.7 billion stars. To visualize and render the simulated data, Jon Parker, the museum’s lead technical director, relied on Houdini, a 3D animation tool by Toronto-based SideFX. The goal is immersion, “whether it’s in front of the buffalo downstairs, and seeing what those herds were like before we decimated them, to coming in this room and being teleported to space, with an accurate foundation in the science,” Emmart says. “But the art is important, because the art is the way to the soul.”  The museum, he adds, is “a testament to wonder. And I think wonder is a gateway to inspiration, and inspiration is a gateway to motivation.” Three-D visuals aren’t just powerful tools for communicating science, but increasingly crucial for science itself. Software like OpenSpace, an open source simulation tool developed by the museum, along with the growing availability of high-performance computing, are making it easier to build highly detailed visuals of ever larger and more complex collections of data. “Anytime we look, literally, from a different angle at catalogs of astronomical positions, simulations, or exploring the phase space of a complex data set, there is great potential to discover something new,” says Brian R. Kent, an astronomer and director of science communications at National Radio Astronomy Observatory. “There is also a wealth of astronomics tatical data in archives that can be reanalyzed in new ways, leading to new discoveries.” As the instruments grow in size and sophistication, so does the data, and the challenge of understanding it. Like all scientists, astronomers are facing a deluge of data, ranging from gamma rays and X-rays to ultraviolet, optical, infrared, and radio bands. Our Oort cloud (center), a shell of icy bodies that surrounds the solar system and extends one-and-a-half light years in every direction, is shown in this scene from ‘Encounters in the Milky Way’ along with the Oort clouds of neighboring stars. The more massive the star, the larger its Oort cloud [Image: © AMNH ] “New facilities like the Next Generation Very Large Array here at NRAO or the Vera Rubin Observatory and LSST survey project will generate large volumes of data, so astronomers have to get creative with how to analyze it,” says Kent.  More data—and new instruments—will also be needed to prove the spiral itself is actually there: there’s still no known way to even observe the Oort cloud.  Instead, the paper notes, the structure will have to be measured from “detection of a large number of objects” in the radius of the inner Oort cloud or from “thermal emission from small particles in the Oort spiral.”  The Vera C. Rubin Observatory, a powerful, U.S.-funded telescope that recently began operation in Chile, could possibly observe individual icy bodies within the cloud. But researchers expect the telescope will likely discover only dozens of these objects, maybe hundreds, not enough to meaningfully visualize any shapes in the Oort cloud.  For us, here and now, the 1.4 trillion mile-long spiral will remain confined to the inside of a dark dome across the street from Central Park.