In a new effort to advance sovereign AI for European public service media, NVIDIA and the European Broadcasting Union (EBU) are working together to give the media industry access to high-quality and trusted cloud and AI technologies. Announced at NVIDIA GTC Paris at VivaTech, NVIDIA’s collaboration with the EBU — the world’s leading alliance of public service media with more than 110 member organizations in 50+ countries, reaching an audience of over 1 billion — focuses on helping build sovereign AI and cloud frameworks, driving workforce development and cultivating an AI ecosystem to create a more equitable, accessible and resilient European media landscape. The work will create better foundations for public service media to benefit from European cloud infrastructure and AI services that are exclusively governed by European policy, comply with European data protection and privacy rules, and embody European values. Sovereign AI ensures nations can develop and deploy artificial intelligence using local infrastructure, datasets and expertise. By investing in it, European countries can preserve their cultural identity, enhance public trust and support innovation specific to their needs. “We are proud to collaborate with NVIDIA to drive the development of sovereign AI and cloud services,” said Michael Eberhard, chief technology officer of public broadcaster ARD/SWR, and chair of the EBU Technical Committee. “By advancing these capabilities together, we’re helping ensure that powerful, compliant and accessible media services are made available to all EBU members — powering innovation, resilience and strategic autonomy across the board.” Empowering Media Innovation in Europe To support the development of sovereign AI technologies, NVIDIA and the EBU will establish frameworks that prioritize independence and public trust, helping ensure that AI serves the interests of Europeans while preserving the autonomy of media organizations. Through this collaboration, NVIDIA and the EBU will develop hybrid cloud architectures designed to meet the highest standards of European public service media. The EBU will contribute its Dynamic Media Facility (DMF) and Media eXchange Layer (MXL) architecture, aiming to enable interoperability and scalability for workflows, as well as cost- and energy-efficient AI training and inference. Following open-source principles, this work aims to create an accessible, dynamic technology ecosystem. The collaboration will also provide public service media companies with the tools to deliver personalized, contextually relevant services and content recommendation systems, with a focus on transparency, accountability and cultural identity. This will be realized through investment in sovereign cloud and AI infrastructure and software platforms such as NVIDIA AI Enterprise, custom foundation models, large language models trained with local data, and retrieval-augmented generation technologies. As part of the collaboration, NVIDIA is also making available resources from its Deep Learning Institute, offering European media organizations comprehensive training programs to create an AI-ready workforce. This will support the EBU’s efforts to help ensure news integrity in the age of AI. In addition, the EBU and its partners are investing in local data centers and cloud platforms that support sovereign technologies, such as NVIDIA GB200 Grace Blackwell Superchip, NVIDIA RTX PRO Servers, NVIDIA DGX Cloud and NVIDIA Holoscan for Media — helping members of the union achieve secure and cost- and energy-efficient AI training, while promoting AI research and development. Partnering With Public Service Media for Sovereign Cloud and AI Collaboration within the media sector is essential for the development and application of comprehensive standards and best practices that ensure the creation and deployment of sovereign European cloud and AI. By engaging with independent software vendors, data center providers, cloud service providers and original equipment manufacturers, NVIDIA and the EBU aim to create a unified approach to sovereign cloud and AI. This work will also facilitate discussions between the cloud and AI industry and European regulators, helping ensure the development of practical solutions that benefit both the general public and media organizations. “Building sovereign cloud and AI capabilities based on EBU’s Dynamic Media Facility and Media eXchange Layer architecture requires strong cross-industry collaboration,” said Antonio Arcidiacono, chief technology and innovation officer at the EBU. “By collaborating with NVIDIA, as well as a broad ecosystem of media technology partners, we are fostering a shared foundation for trust, innovation and resilience that supports the growth of European media.” Learn more about the EBU. Watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang at VivaTech, and explore GTC Paris sessions. 

At GTC Paris — held alongside VivaTech, Europe’s largest tech event — NVIDIA founder and CEO Jensen Huang delivered a clear message: Europe isn’t just adopting AI — it’s building it. “We now have a new industry, an AI industry, and it’s now part of the new infrastructure, called intelligence infrastructure, that will be used by every country, every society,” Huang said, addressing an audience gathered online and at the iconic Dôme de Paris. From exponential inference growth to quantum breakthroughs, and from infrastructure to industry, agentic AI to robotics, Huang outlined how the region is laying the groundwork for an AI-powered future. A New Industrial Revolution At the heart of this transformation, Huang explained, are systems like GB200 NVL72 — “one giant GPU” and NVIDIA’s most powerful AI platform yet — now in full production and powering everything from sovereign models to quantum computing. “This machine was designed to be a thinking machine, a thinking machine, in the sense that it reasons, it plans, it spends a lot of time talking to itself,” Huang said, walking the audience through the size and scale of these machines and their performance. At GTC Paris, Huang showed audience members the innards of some of NVIDIA’s latest hardware. There’s more coming, with Huang saying NVIDIA’s partners are now producing 1,000 GB200 systems a week, “and this is just the beginning.” He walked the audience through a range of available systems ranging from the tiny NVIDIA DGX Spark to rack-mounted RTX PRO Servers. Huang explained that NVIDIA is working to help countries use technologies like these to build both AI infrastructure — services built for third parties to use and innovate on — and AI factories, which companies build for their own use, to generate revenue. NVIDIA is partnering with European governments, telcos and cloud providers to deploy NVIDIA technologies across the region. NVIDIA is also expanding its network of technology centers across Europe — including new hubs in Finland, Germany, Spain, Italy and the U.K. — to accelerate skills development and quantum growth. Quantum Meets Classical Europe’s quantum ambitions just got a boost. The NVIDIA CUDA-Q platform is live on Denmark’s Gefion supercomputer, opening new possibilities for hybrid AI and quantum engineering. In addition, Huang announced that CUDA-Q is now available on NVIDIA Grace Blackwell systems. Across the continent, NVIDIA is partnering with supercomputing centers and quantum hardware builders to advance hybrid quantum-AI research and accelerate quantum error correction. “Quantum computing is reaching an inflection point,” Huang said. “We are within reach of being able to apply quantum computing, quantum classical computing, in areas that can solve some interesting problems in the coming years.” Sovereign Models, Smarter Agents European developers want more control over their models. Enter NVIDIA Nemotron, designed to help build large language models tuned to local needs. “And so now you know that you have access to an enhanced open model that is still open, that is top of the leader chart,” Huang said. These models will be coming to Perplexity, a reasoning search engine, enabling secure, multilingual AI deployment across Europe. “You can now ask and get questions answered in the language, in the culture, in the sensibility of your country,” Huang said. Huang explained how NVIDIA is helping countries across Europe build AI infrastructure. Every company will build its own agents, Huang said. To help create those agents, Huang introduced a suite of agentic AI blueprints, including an Agentic AI Safety blueprint for enterprises and governments. The new NVIDIA NeMo Agent toolkit and NVIDIA AI Blueprint for building data flywheels further accelerate the development of safe, high-performing AI agents. To help deploy these agents, NVIDIA is partnering with European governments, telcos and cloud providers to deploy the DGX Cloud Lepton platform across the region, providing instant access to accelerated computing capacity. “One model architecture, one deployment, and you can run it anywhere,” Huang said, adding that Lepton is now integrated with Hugging Face, giving developers direct access to global compute. The Industrial Cloud Goes Live AI isn’t just virtual. It’s powering physical systems, too, sparking a new industrial revolution. “We’re working on industrial AI with one company after another,” Huang said, describing work to build digital twins based on the NVIDIA Omniverse platform with companies across the continent. Huang explained that everything he showed during his keynote was “computer simulation, not animation” and that it looks beautiful because “it turns out the world is beautiful, and it turns out math is beautiful.” To further this work, Huang announced NVIDIA is launching the world’s first industrial AI cloud — to be built in Germany — to help Europe’s manufacturers simulate, automate and optimize at scale. “Soon, everything that moves will be robotic,” Huang said. “And the car is the next one.” NVIDIA DRIVE, NVIDIA’s full-stack AV platform, is now in production to accelerate the large-scale deployment of safe, intelligent transportation. And to show what’s coming next, Huang was joined on stage by Grek, a pint-sized robot, as Huang talked about how NVIDIA partnered with DeepMind and Disney to build Newton, the world’s most advanced physics training engine for robotics. The Next Wave The next wave of AI has begun — and it’s exponential, Huang explained. “We have physical robots, and we have information robots. We call them agents,” Huang said. “The technology necessary to teach a robot to manipulate, to simulate — and of course, the manifestation of an incredible robot — is now right in front of us.” This new era of AI is being driven by a surge in inference workloads. “The number of people using inference has gone from 8 million to 800 million — 100x in just a couple of years,” Huang said. To meet this demand, Huang emphasized the need for a new kind of computer: “We need a special computer designed for thinking, designed for reasoning. And that’s what Blackwell is — a thinking machine.” Huang and Grek, as he explained how AI is driving advancements in robotics. These Blackwell-powered systems will live in a new class of data centers — AI factories — built to generate tokens, the raw material of modern intelligence. “These AI factories are going to generate tokens,” Huang said, turning to Grek with a smile. “And these tokens are going to become your food, little Grek.” With that, the keynote closed on a bold vision: a future powered by sovereign infrastructure, agentic AI, robotics — and exponential inference — all built in partnership with Europe. Watch the NVIDIA GTC Paris keynote from 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.

From the heart of Germany’s automotive sector to manufacturing hubs across France and Italy, Europe is embracing industrial AI and advanced AI-powered robotics to address labor shortages, boost productivity and fuel sustainable economic growth. Robotics companies are developing humanoid robots and collaborative systems that integrate AI into real-world manufacturing applications. Supported by a $200 billion investment initiative and coordinated efforts from the European Commission, Europe is positioning itself at the forefront of the next wave of industrial automation, powered by AI. This momentum is on full display at Automatica — Europe’s premier conference on advancements in robotics, machine vision and intelligent manufacturing — taking place this week in Munich, Germany. NVIDIA and its ecosystem of partners and customers are showcasing next-generation robots, automation and AI technologies designed to accelerate the continent’s leadership in smart manufacturing and logistics. NVIDIA Technologies Boost Robotics Development  Central to advancing robotics development is Europe’s first industrial AI cloud, announced at NVIDIA GTC Paris at VivaTech earlier this month. The Germany-based AI factory, featuring 10,000 NVIDIA GPUs, provides European manufacturers with secure, sovereign and centralized AI infrastructure for industrial workloads. It will support applications ranging from design and engineering to factory digital twins and robotics. To help accelerate humanoid development, NVIDIA released NVIDIA Isaac GR00T N1.5 — an open foundation model for humanoid robot reasoning and skills. This update enhances the model’s adaptability and ability to follow instructions, significantly improving its performance in material handling and manufacturing tasks. To help post-train GR00T N1.5, NVIDIA has also released the Isaac GR00T-Dreams blueprint — a reference workflow for generating vast amounts of synthetic trajectory data from a small number of human demonstrations — enabling robots to generalize across behaviors and adapt to new environments with minimal human demonstration data. In addition, early developer previews of NVIDIA Isaac Sim 5.0 and Isaac Lab 2.2 — open-source robot simulation and learning frameworks optimized for NVIDIA RTX PRO 6000 workstations — are now available on GitHub. Image courtesy of Wandelbots. Robotics Leaders Tap NVIDIA Simulation Technology to Develop and Deploy Humanoids and More  Robotics developers and solutions providers across the globe are integrating NVIDIA’s three computers to train, simulate and deploy robots. NEURA Robotics, a German robotics company and pioneer for cognitive robots, unveiled the third generation of its humanoid, 4NE1, designed to assist humans in domestic and professional environments through advanced cognitive capabilities and humanlike interaction. 4NE1 is powered by GR00T N1 and was trained in Isaac Sim and Isaac Lab before real-world deployment. NEURA Robotics is also presenting Neuraverse, a digital twin and interconnected ecosystem for robot training, skills and applications, fully compatible with NVIDIA Omniverse technologies. Delta Electronics, a global leader in power management and smart green solutions, is debuting two next-generation collaborative robots: D-Bot Mar and D-Bot 2 in 1 — both trained using Omniverse and Isaac Sim technologies and libraries. These cobots are engineered to transform intralogistics and optimize production flows. Wandelbots, the creator of the Wandelbots NOVA software platform for industrial robotics, is partnering with SoftServe, a global IT consulting and digital services provider, to scale simulation-first automating using NVIDIA Isaac Sim, enabling virtual validation and real-world deployment with maximum impact. Cyngn, a pioneer in autonomous mobile robotics, is integrating its DriveMod technology into Isaac Sim to enable large-scale, high fidelity virtual testing of advanced autonomous operation. Purpose-built for industrial applications, DriveMod is already deployed on vehicles such as the Motrec MT-160 Tugger and BYD Forklift, delivering sophisticated automation to material handling operations. Doosan Robotics, a company specializing in AI robotic solutions, will showcase its “sim to real” solution, using NVIDIA Isaac Sim and cuRobo. Doosan will be showcasing how to seamlessly transfer tasks from simulation to real robots across a wide range of applications — from manufacturing to service industries. Franka Robotics has integrated Isaac GR00T N1.5 into a dual-arm Franka Research 3 (FR3) robot for robotic control. The integration of GR00T N1.5 allows the system to interpret visual input, understand task context and autonomously perform complex manipulation — without the need for task-specific programming or hardcoded logic. Image courtesy of Franka Robotics. Hexagon, the global leader in measurement technologies, launched its new humanoid, dubbed AEON. With its unique locomotion system and multimodal sensor fusion, and powered by NVIDIA’s three-computer solution, AEON is engineered to perform a wide range of industrial applications, from manipulation and asset inspection to reality capture and operator support. Intrinsic, a software and AI robotics company, is integrating Intrinsic Flowstate with  Omniverse and OpenUSD for advanced visualization and digital twins that can be used in many industrial use cases. The company is also using NVIDIA foundation models to enhance robot capabilities like grasp planning through AI and simulation technologies. SCHUNK, a global leader in gripping systems and automation technology, is showcasing its innovative grasping kit powered by the NVIDIA Jetson AGX Orin module. The kit intelligently detects objects and calculates optimal grasping points. Schunk is also demonstrating seamless simulation-to-reality transfer using IGS Virtuous software — built on Omniverse technologies — to control a real robot through simulation in a pick-and-place scenario. Universal Robots is showcasing UR15, its fastest cobot yet. Powered by the UR AI Accelerator — developed with NVIDIA and running on Jetson AGX Orin using CUDA-accelerated Isaac libraries — UR15 helps set a new standard for industrial automation. Vention, a full-stack software and hardware automation company, launched its Machine Motion AI, built on CUDA-accelerated Isaac libraries and powered by Jetson. Vention is also expanding its lineup of robotic offerings by adding the FR3 robot from Franka Robotics to its ecosystem, enhancing its solutions for academic and research applications. Image courtesy of Vention. Learn more about the latest robotics advancements by joining NVIDIA at Automatica, running through Friday, June 27. 

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.

Mark Theriault founded the startup FITY envisioning a line of clever cooling products: cold drink holders that come with freezable pucks to keep beverages cold for longer without the mess of ice. The entrepreneur started with 3D prints of products in his basement, building one unit at a time, before eventually scaling to mass production. Founding a consumer product company from scratch was a tall order for a single person. Going from preliminary sketches to production-ready designs was a major challenge. To bring his creative vision to life, Theriault relied on AI and his NVIDIA GeForce RTX-equipped system. For him, AI isn’t just a tool — it’s an entire pipeline to help him accomplish his goals. Read more about his workflow below. Plus, GeForce RTX 5050 laptops start arriving today at retailers worldwide, from $999. GeForce RTX 5050 Laptop GPUs feature 2,560 NVIDIA Blackwell CUDA cores, fifth-generation AI Tensor Cores, fourth-generation RT Cores, a ninth-generation NVENC encoder and a sixth-generation NVDEC decoder. In addition, NVIDIA’s Plug and Play: Project G-Assist Plug-In Hackathon — running virtually through Wednesday, July 16 — invites developers to explore AI and build custom G-Assist plug-ins for a chance to win prizes. Save the date for the G-Assist Plug-In webinar on Wednesday, July 9, from 10-11 a.m. PT, to learn more about Project G-Assist capabilities and fundamentals, and to participate in a live Q&A session. From Concept to Completion To create his standout products, Theriault tinkers with potential FITY Flex cooler designs with traditional methods, from sketch to computer-aided design to rapid prototyping, until he finds the right vision. A unique aspect of the FITY Flex design is that it can be customized with fun, popular shoe charms. For packaging design inspiration, Theriault uses his preferred text-to-image generative AI model for prototyping, Stable Diffusion XL — which runs 60% faster with the NVIDIA TensorRT software development kit — using the modular, node-based interface ComfyUI. ComfyUI gives users granular control over every step of the generation process — prompting, sampling, model loading, image conditioning and post-processing. It’s ideal for advanced users like Theriault who want to customize how images are generated. Theriault’s uses of AI result in a complete computer graphics-based ad campaign. Image courtesy of FITY. NVIDIA and GeForce RTX GPUs based on the NVIDIA Blackwell architecture include fifth-generation Tensor Cores designed to accelerate AI and deep learning workloads. These GPUs work with CUDA optimizations in PyTorch to seamlessly accelerate ComfyUI, reducing generation time on FLUX.1-dev, an image generation model from Black Forest Labs, from two minutes per image on the Mac M3 Ultra to about four seconds on the GeForce RTX 5090 desktop GPU. ComfyUI can also add ControlNets — AI models that help control image generation — that Theriault uses for tasks like guiding human poses, setting compositions via depth mapping and converting scribbles to images. Theriault even creates his own fine-tuned models to keep his style consistent. He used low-rank adaptation (LoRA) models — small, efficient adapters into specific layers of the network — enabling hyper-customized generation with minimal compute cost. LoRA models allow Theriault to ideate on visuals quickly. Image courtesy of FITY. “Over the last few months, I’ve been shifting from AI-assisted computer graphics renders to fully AI-generated product imagery using a custom Flux LoRA I trained in house. My RTX 4080 SUPER GPU has been essential for getting the performance I need to train and iterate quickly.” – Mark Theriault, founder of FITY  Theriault also taps into generative AI to create marketing assets like FITY Flex product packaging. He uses FLUX.1, which excels at generating legible text within images, addressing a common challenge in text-to-image models. Though FLUX.1 models can typically consume over 23GB of VRAM, NVIDIA has collaborated with Black Forest Labs to help reduce the size of these models using quantization — a technique that reduces model size while maintaining quality. The models were then accelerated with TensorRT, which provides an up to 2x speedup over PyTorch. To simplify using these models in ComfyUI, NVIDIA created the FLUX.1 NIM microservice, a containerized version of FLUX.1 that can be loaded in ComfyUI and enables FP4 quantization and TensorRT support. Combined, the models come down to just over 11GB of VRAM, and performance improves by 2.5x. Theriault uses the Blender Cycles app to render out final files. For 3D workflows, NVIDIA offers the AI Blueprint for 3D-guided generative AI to ease the positioning and composition of 3D images, so anyone interested in this method can quickly get started. Photorealistic renders. Image courtesy of FITY. Finally, Theriault uses large language models to generate marketing copy — tailored for search engine optimization, tone and storytelling — as well as to complete his patent and provisional applications, work that usually costs thousands of dollars in legal fees and considerable time. Generative AI helps Theriault create promotional materials like the above. Image courtesy of FITY. “As a one-man band with a ton of content to generate, having on-the-fly generation capabilities for my product designs really helps speed things up.” – Mark Theriault, founder of FITY Every texture, every word, every photo, every accessory was a micro-decision, Theriault said. AI helped him survive the “death by a thousand cuts” that can stall solo startup founders, he added. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.  Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X.  See notice regarding software product information.

By TREVOR HOGG Images courtesy of Warner Bros. Pictures. Rather than a world constructed around photorealistic pixels, a video game created by Markus Persson has taken the boxier 3D voxel route, which has become its signature aesthetic, and sparked an international phenomenon that finally gets adapted into a feature with the release of A Minecraft Movie. Brought onboard to help filmmaker Jared Hess in creating the environments that the cast of Jason Momoa, Jack Black, Sebastian Hansen, Emma Myers and Danielle Brooks find themselves inhabiting was Disguise under the direction of Production VFX Supervisor Dan Lemmon. “[A]s the Senior Unreal Artist within the Virtual Art Department (VAD) on Minecraft, I experienced the full creative workflow. What stood out most was how deeply the VAD was embedded across every stage of production. We weren’t working in isolation. From the production designer and director to the VFX supervisor and DP, the VAD became a hub for collaboration.” —Talia Finlayson, Creative Technologist, Disguise Interior and exterior environments had to be created, such as the shop owned by Steve (Jack Black). “Prior to working on A Minecraft Movie, I held more technical roles, like serving as the Virtual Production LED Volume Operator on a project for Apple TV+ and Paramount Pictures,” notes Talia Finlayson, Creative Technologist for Disguise. “But as the Senior Unreal Artist within the Virtual Art Department (VAD) on Minecraft, I experienced the full creative workflow. What stood out most was how deeply the VAD was embedded across every stage of production. We weren’t working in isolation. From the production designer and director to the VFX supervisor and DP, the VAD became a hub for collaboration.” The project provided new opportunities. “I’ve always loved the physicality of working with an LED volume, both for the immersion it provides and the way that seeing the environment helps shape an actor’s performance,” notes Laura Bell, Creative Technologist for Disguise. “But for A Minecraft Movie, we used Simulcam instead, and it was an incredible experience to live-composite an entire Minecraft world in real-time, especially with nothing on set but blue curtains.” Set designs originally created by the art department in Rhinoceros 3D were transformed into fully navigable 3D environments within Unreal Engine. “These scenes were far more than visualizations,” Finlayson remarks. “They were interactive tools used throughout the production pipeline. We would ingest 3D models and concept art, clean and optimize geometry using tools like Blender, Cinema 4D or Maya, then build out the world in Unreal Engine. This included applying materials, lighting and extending environments. These Unreal scenes we created were vital tools across the production and were used for a variety of purposes such as enabling the director to explore shot compositions, block scenes and experiment with camera movement in a virtual space, as well as passing along Unreal Engine scenes to the visual effects vendors so they could align their digital environments and set extensions with the approved production layouts.” A virtual exploration of Steve’s shop in Midport Village. Certain elements have to be kept in mind when constructing virtual environments. “When building virtual environments, you need to consider what can actually be built, how actors and cameras will move through the space, and what’s safe and practical on set,” Bell observes. “Outside the areas where strict accuracy is required, you want the environments to blend naturally with the original designs from the art department and support the story, creating a space that feels right for the scene, guides the audience’s eye and sets the right tone. Things like composition, lighting and small environmental details can be really fun to work on, but also serve as beautiful additions to help enrich a story.” “I’ve always loved the physicality of working with an LED volume, both for the immersion it provides and the way that seeing the environment helps shape an actor’s performance. But for A Minecraft Movie, we used Simulcam instead, and it was an incredible experience to live-composite an entire Minecraft world in real-time, especially with nothing on set but blue curtains.” —Laura Bell, Creative Technologist, Disguise Among the buildings that had to be created for Midport Village was Steve’s (Jack Black) Lava Chicken Shack. Concept art was provided that served as visual touchstones. “We received concept art provided by the amazing team of concept artists,” Finlayson states. “Not only did they send us 2D artwork, but they often shared the 3D models they used to create those visuals. These models were incredibly helpful as starting points when building out the virtual environments in Unreal Engine; they gave us a clear sense of composition and design intent. Storyboards were also a key part of the process and were constantly being updated as the project evolved. Having access to the latest versions allowed us to tailor the virtual environments to match camera angles, story beats and staging. Sometimes we would also help the storyboard artists by sending through images of the Unreal Engine worlds to help them geographically position themselves in the worlds and aid in their storyboarding.” At times, the video game assets came in handy. “Exteriors often involved large-scale landscapes and stylized architectural elements, which had to feel true to the Minecraft world,” Finlayson explains. “In some cases, we brought in geometry from the game itself to help quickly block out areas. For example, we did this for the Elytra Flight Chase sequence, which takes place through a large canyon.” Flexibility was critical. “A key technical challenge we faced was ensuring that the Unreal levels were built in a way that allowed for fast and flexible iteration,” Finlayson remarks. “Since our environments were constantly being reviewed by the director, production designer, DP and VFX supervisor, we needed to be able to respond quickly to feedback, sometimes live during a review session. To support this, we had to keep our scenes modular and well-organized; that meant breaking environments down into manageable components and maintaining clean naming conventions. By setting up the levels this way, we could make layout changes, swap assets or adjust lighting on the fly without breaking the scene or slowing down the process.” Production schedules influence the workflows, pipelines and techniques. “No two projects will ever feel exactly the same,” Bell notes. “For example, Pat Younis [VAD Art Director] adapted his typical VR setup to allow scene reviews using a PS5 controller, which made it much more comfortable and accessible for the director. On a more technical side, because everything was cubes and voxels, my Blender workflow ended up being way heavier on the re-mesh modifier than usual, definitely not something I’ll run into again anytime soon!” A virtual study and final still of the cast members standing outside of the Lava Chicken Shack. “We received concept art provided by the amazing team of concept artists. Not only did they send us 2D artwork, but they often shared the 3D models they used to create those visuals. These models were incredibly helpful as starting points when building out the virtual environments in Unreal Engine; they gave us a clear sense of composition and design intent. Storyboards were also a key part of the process and were constantly being updated as the project evolved. Having access to the latest versions allowed us to tailor the virtual environments to match camera angles, story beats and staging.” —Talia Finlayson, Creative Technologist, Disguise The design and composition of virtual environments tended to remain consistent throughout principal photography. “The only major design change I can recall was the removal of a second story from a building in Midport Village to allow the camera crane to get a clear shot of the chicken perched above Steve’s lava chicken shack,” Finlayson remarks. “I would agree that Midport Village likely went through the most iterations,” Bell responds. “The archway, in particular, became a visual anchor across different levels. We often placed it off in the distance to help orient both ourselves and the audience and show how far the characters had traveled. I remember rebuilding the stairs leading up to the rampart five or six times, using different configurations based on the physically constructed stairs. This was because there were storyboarded sequences of the film’s characters, Henry, Steve and Garrett, being chased by piglins, and the action needed to match what could be achieved practically on set.” Virtually conceptualizing the layout of Midport Village. Complex virtual environments were constructed for the final battle and the various forest scenes throughout the movie. “What made these particularly challenging was the way physical set pieces were repurposed and repositioned to serve multiple scenes and locations within the story,” Finlayson reveals. “The same built elements had to appear in different parts of the world, so we had to carefully adjust the virtual environments to accommodate those different positions.” Bell is in agreement with her colleague. “The forest scenes were some of the more complex environments to manage. It could get tricky, particularly when the filming schedule shifted. There was one day on set where the order of shots changed unexpectedly, and because the physical sets looked so similar, I initially loaded a different perspective than planned. Fortunately, thanks to our workflow, Lindsay George [VP Tech] and I were able to quickly open the recorded sequence in Unreal Engine and swap out the correct virtual environment for the live composite without any disruption to the shoot.” An example of the virtual and final version of the Woodland Mansion. “Midport Village likely went through the most iterations. The archway, in particular, became a visual anchor across different levels. We often placed it off in the distance to help orient both ourselves and the audience and show how far the characters had traveled.” —Laura Bell, Creative Technologist, Disguise Extensive detail was given to the center of the sets where the main action unfolds. “For these areas, we received prop layouts from the prop department to ensure accurate placement and alignment with the physical builds,” Finlayson explains. “These central environments were used heavily for storyboarding, blocking and department reviews, so precision was essential. As we moved further out from the practical set, the environments became more about blocking and spatial context rather than fine detail. We worked closely with Production Designer Grant Major to get approval on these extended environments, making sure they aligned with the overall visual direction. We also used creatures and crowd stand-ins provided by the visual effects team. These gave a great sense of scale and placement during early planning stages and allowed other departments to better understand how these elements would be integrated into the scenes.” Cast members Sebastian Hansen, Danielle Brooks and Emma Myers stand in front of the Earth Portal Plateau environment. Doing a virtual scale study of the Mountainside. Practical requirements like camera moves, stunt choreography and crane setups had an impact on the creation of virtual environments. “Sometimes we would adjust layouts slightly to open up areas for tracking shots or rework spaces to accommodate key action beats, all while keeping the environment feeling cohesive and true to the Minecraft world,” Bell states. “Simulcam bridged the physical and virtual worlds on set, overlaying Unreal Engine environments onto live-action scenes in real-time, giving the director, DP and other department heads a fully-realized preview of shots and enabling precise, informed decisions during production. It also recorded critical production data like camera movement paths, which was handed over to the post-production team to give them the exact tracks they needed, streamlining the visual effects pipeline.” Piglots cause mayhem during the Wingsuit Chase. Virtual versions of the exterior and interior of the Safe House located in the Enchanted Woods. “One of the biggest challenges for me was managing constant iteration while keeping our environments clean, organized and easy to update,” Finlayson notes. “Because the virtual sets were reviewed regularly by the director and other heads of departments, feedback was often implemented live in the room. This meant the environments had to be flexible. But overall, this was an amazing project to work on, and I am so grateful for the incredible VAD team I was a part of – Heide Nichols [VAD Supervisor], Pat Younis, Jake Tuck [Unreal Artist] and Laura. Everyone on this team worked so collaboratively, seamlessly and in such a supportive way that I never felt like I was out of my depth.” There was another challenge that is more to do with familiarity. “Having a VAD on a film is still a relatively new process in production,” Bell states. “There were moments where other departments were still learning what we did and how to best work with us. That said, the response was overwhelmingly positive. I remember being on set at the Simulcam station and seeing how excited people were to look at the virtual environments as they walked by, often stopping for a chat and a virtual tour. Instead of seeing just a huge blue curtain, they were stoked to see something Minecraft and could get a better sense of what they were actually shooting.”

Yoji shows how to prepare generated models for proper texturing in tools like Substance Painter. Source

Parece que la inteligencia artificial ha vuelto a demostrar su eficacia en el sector de la impresión 3D, concretamente en la fase de diseño. Un equipo ha utilizado la IA para desarrollar un generador de modelos 3D capaz de sintetizar…

En partenariat avec Revopoint. Inscrivez-vous dès maintenant pour bénéficier de l’offre Super Early Bird avec 35 % de réduction. Revopoint, leader mondial des solutions de numérisation 3D professionnelles, annonce le lancement du scanner 3D avec suiv