NVIDIA was today named an Autonomous Grand Challenge winner at the Computer Vision and Pattern Recognition (CVPR) conference, held this week in Nashville, Tennessee. The announcement was made at the Embodied Intelligence for Autonomous Systems on the Horizon Workshop. This marks the second consecutive year that NVIDIA’s topped the leaderboard in the End-to-End Driving at Scale category and the third year in a row winning an Autonomous Grand Challenge award at CVPR. The theme of this year’s challenge was “Towards Generalizable Embodied Systems” — based on NAVSIM v2, a data-driven, nonreactive autonomous vehicle (AV) simulation framework. The challenge offered researchers the opportunity to explore ways to handle unexpected situations, beyond using only real-world human driving data, to accelerate the development of smarter, safer AVs. Generating Safe and Adaptive Driving Trajectories Participants of the challenge were tasked with generating driving trajectories from multi-sensor data in a semi-reactive simulation, where the ego vehicle’s plan is fixed at the start, but background traffic changes dynamically. Submissions were evaluated using the Extended Predictive Driver Model Score, which measures safety, comfort, compliance and generalization across real-world and synthetic scenarios — pushing the boundaries of robust and generalizable autonomous driving research. The NVIDIA AV Applied Research Team’s key innovation was the Generalized Trajectory Scoring (GTRS) method, which generates a variety of trajectories and progressively filters out the best one. GTRS model architecture showing a unified system for generating and scoring diverse driving trajectories using diffusion- and vocabulary-based trajectories. GTRS introduces a combination of coarse sets of trajectories covering a wide range of situations and fine-grained trajectories for safety-critical situations, created using a diffusion policy conditioned on the environment. GTRS then uses a transformer decoder distilled from perception-dependent metrics, focusing on safety, comfort and traffic rule compliance. This decoder progressively filters out the most promising trajectory candidates by capturing subtle but critical differences between similar trajectories. This system has proved to generalize well to a wide range of scenarios, achieving state-of-the-art results on challenging benchmarks and enabling robust, adaptive trajectory selection in diverse and challenging driving conditions. NVIDIA Automotive Research at CVPR  More than 60 NVIDIA papers were accepted for CVPR 2025, spanning automotive, healthcare, robotics and more. In automotive, NVIDIA researchers are advancing physical AI with innovation in perception, planning and data generation. This year, three NVIDIA papers were nominated for the Best Paper Award: FoundationStereo, Zero-Shot Monocular Scene Flow and Difix3D+. The NVIDIA papers listed below showcase breakthroughs in stereo depth estimation, monocular motion understanding, 3D reconstruction, closed-loop planning, vision-language modeling and generative simulation — all critical to building safer, more generalizable AVs: Diffusion Renderer: Neural Inverse and Forward Rendering With Video Diffusion Models (Read more in this blog.) FoundationStereo: Zero-Shot Stereo Matching (Best Paper nominee) Zero-Shot Monocular Scene Flow Estimation in the Wild (Best Paper nominee) Difix3D+: Improving 3D Reconstructions With Single-Step Diffusion Models (Best Paper nominee) 3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models Zero-Shot 4D Lidar Panoptic Segmentation NVILA: Efficient Frontier Visual Language Models RADIO Amplified: Improved Baselines for Agglomerative Vision Foundation Models OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving With Counterfactual Reasoning Explore automotive workshops and tutorials at CVPR, including: Workshop on Data-Driven Autonomous Driving Simulation, featuring Marco Pavone, senior director of AV research at NVIDIA, and Sanja Fidler, vice president of AI research at NVIDIA Workshop on Autonomous Driving, featuring Laura Leal-Taixe, senior research manager at NVIDIA Workshop on Open-World 3D Scene Understanding with Foundation Models, featuring Leal-Taixe Safe Artificial Intelligence for All Domains, featuring Jose Alvarez, director of AV applied research at NVIDIA Workshop on Foundation Models for V2X-Based Cooperative Autonomous Driving, featuring Pavone and Leal-Taixe Workshop on Multi-Agent Embodied Intelligent Systems Meet Generative AI Era, featuring Pavone LatinX in CV Workshop, featuring Leal-Taixe Workshop on Exploring the Next Generation of Data, featuring Alvarez Full-Stack, GPU-Based Acceleration of Deep Learning and Foundation Models, led by NVIDIA Continuous Data Cycle via Foundation Models, led by NVIDIA Distillation of Foundation Models for Autonomous Driving, led by NVIDIA Explore the NVIDIA research papers to be presented at CVPR and watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang. Learn more about NVIDIA Research, a global team of hundreds of scientists and engineers focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics. The featured image above shows how an autonomous vehicle adapts its trajectory to navigate an urban environment with dynamic traffic using the GTRS model.

In the face of growing labor shortages and need for sustainability, European manufacturers are racing to reinvent their processes to become software-defined and AI-driven. To achieve this, robot developers and industrial digitalization solution providers are working with NVIDIA to build safe, AI-driven robots and industrial technologies to drive modern, sustainable manufacturing. At NVIDIA GTC Paris at VivaTech, Europe’s leading robotics companies including Agile Robots, Extend Robotics, Humanoid, idealworks, Neura Robotics, SICK, Universal Robots, Vorwerk and Wandelbots are showcasing their latest AI-driven robots and automation breakthroughs, all accelerated by NVIDIA technologies. In addition, NVIDIA is releasing new models and tools to support the entire robotics ecosystem. NVIDIA Releases Tools for Accelerating Robot Development and Safety NVIDIA Isaac GR00T N1.5, an open foundation model for humanoid robot reasoning and skills, is now available for download on Hugging Face. This update enhances the model’s adaptability and ability to follow instructions, significantly improving its performance in material handling and manufacturing tasks. The NVIDIA Isaac Sim 5.0 and Isaac Lab 2.2 open-source robotics simulation and learning frameworks, optimized for NVIDIA RTX PRO 6000 workstations, are available on GitHub for developer preview. In addition, NVIDIA announced that NVIDIA Halos — a full-stack, comprehensive safety system that unifies hardware architecture, AI models, software, tools and services — now expands to robotics, promoting safety across the entire development lifecycle of AI-driven robots. The NVIDIA Halos AI Systems Inspection Lab has earned accreditation from the ANSI National Accreditation Board (ANAB) to perform inspections across functional safety for robotics, in addition to automotive vehicles. “NVIDIA’s latest evaluation with ANAB verifies the demonstration of competence and compliance with internationally recognized standards, helping ensure that developers of autonomous machines — from automotive to robotics — can meet the highest benchmarks for functional safety,” said R. Douglas Leonard Jr., executive director of ANAB. Arcbest, Advantech, Bluewhite, Boston Dynamics, FORT, Inxpect, KION, NexCobot — a NEXCOM company, and Synapticon are among the first robotics companies to join the Halos Inspection Lab, ensuring their products meet NVIDIA safety and cybersecurity requirements. To support robotics leaders in strengthening safety across the entire development lifecycle of AI-driven robots, Halos will now provide: Safety extension packages for the NVIDIA IGX platform, enabling manufacturers to easily program safety functions into their robots, supported by TÜV Rheinland’s inspection of NVIDIA IGX. A robotic safety platform, which includes IGX and NVIDIA Holoscan Sensor Bridge for a unified approach to designing sensor-to-compute architecture with built-in AI safety. An outside-in safety AI inspector — an AI-powered agent for monitoring robot operations, helping improve worker safety. Europe’s Robotics Ecosystem Builds on NVIDIA’s Three Computers Europe’s leading robotics developers and solution providers are integrating the NVIDIA Isaac robotics platform to train, simulate and deploy robots across different embodiments. Agile Robots is post-training the GR00T N1 model in Isaac Lab to train its dual-arm manipulator robots, which run on NVIDIA Jetson hardware, to execute a variety of tasks in industrial environments. Meanwhile, idealworks has adopted the Mega NVIDIA Omniverse Blueprint for robotic fleet simulation to extend the blueprint’s capabilities to humanoids. Building on the VDA 5050 framework, idealworks contributes to the development of guidance that supports tasks uniquely enabled by humanoid robots, such as picking, moving and placing objects. Neura Robotics is integrating NVIDIA Isaac to further enhance its robot development workflows. The company is using GR00T-Mimic to post-train the Isaac GR00T N1 robot foundation model for its service robot MiPA. Neura is also collaborating with SAP and NVIDIA to integrate SAP’s Joule agents with its robots, using the Mega NVIDIA Omniverse Blueprint to simulate and refine robot behavior in complex, realistic operational scenarios before deployment. Vorwerk is using NVIDIA technologies to power its AI-driven collaborative robots. The company is post-training GR00T N1 models in Isaac Lab with its custom synthetic data pipeline, which is built on Isaac GR00T-Mimic and powered by the NVIDIA Omniverse platform. The enhanced models are then deployed on NVIDIA Jetson AGX, Jetson Orin or Jetson Thor modules for advanced, real-time home robotics. Humanoid is using NVIDIA’s full robotics stack, including Isaac Sim and Isaac Lab, to cut its prototyping time down by six weeks. The company is training its vision language action models on NVIDIA DGX B200 systems to boost the cognitive abilities of its robots, allowing them to operate autonomously in complex environments using Jetson Thor onboard computing. Universal Robots is introducing UR15, its fastest collaborative robot yet, to the European market. Using UR’s AI Accelerator — developed on NVIDIA Isaac’s CUDA-accelerated libraries and AI models, as well as NVIDIA Jetson AGX Orin — manufacturers can build AI applications to embed intelligence into the company’s new cobots. Wandelbots is showcasing its NOVA Operating System, now integrated with Omniverse, to simulate, validate and optimize robotic behaviors virtually before deploying them to physical robots. Wandelbots also announced a collaboration with EY and EDAG to offer manufacturers a scalable automation platform on Omniverse that speeds up the transition from proof of concept to full-scale deployment. Extend Robotics is using the Isaac GR00T platform to enable customers to control and train robots for industrial tasks like visual inspection and handling radioactive materials. The company’s Advanced Mechanics Assistance System lets users collect demonstration data and generate diverse synthetic datasets with NVIDIA GR00T-Mimic and GR00T-Gen to train the GR00T N1 foundation model. SICK is enhancing its autonomous perception solutions by integrating new certified sensor models — as well as 2D and 3D lidars, safety scanners and cameras — into NVIDIA Isaac Sim. This enables engineers to virtually design, test and validate machines using SICK’s sensing models within Omniverse, supporting processes spanning product development to large-scale robotic fleet management. Toyota Material Handling Europe is working with SoftServe to simulate its autonomous mobile robots working alongside human workers, using the Mega NVIDIA Omniverse Blueprint. Toyota Material Handling Europe is testing and simulating a multitude of traffic scenarios — allowing the company to refine its AI algorithms before real-world deployment. NVIDIA’s partner ecosystem is enabling European industries to tap into intelligent, AI-powered robotics. By harnessing advanced simulation, digital twins and generative AI, manufacturers are rapidly developing and deploying safe, adaptable robot fleets that address labor shortages, boost sustainability and drive operational efficiency. Watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang at VivaTech, and explore GTC Paris sessions. See notice regarding software product information.

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.

Warning! We're about to go into deep endgame spoilers for Blue Prince, well beyond rolling the credits by reaching Room 46. Read on at your own risk.I had been playing Blue Prince for more than 100 hours before I felt like I truly understood what the game was really about.The revelation came in the form of a journal entry, secreted away in a safety deposit box, hidden within the sometimes tough-to-access vault of the strange and shifting Mount Holly Manor. Reaching the paper requires solving one of Blue Prince's toughest, most obtuse, and most rewarding puzzles, one you won't even realize exists until you've broken through riddle after riddle and uncovered mystery after mystery. It recontextualizes everything that has come before it, not only the winding and involved test of wits that is the manor itself, but the story that had to be similarly excavated along the way--one of political intrigue and family tragedy, the rising and falling of kingdoms, the stoking of revolution, and the sacrifice necessary to breathe life into ideals.Continue Reading at GameSpot

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.

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.

By TREVOR HOGG Images courtesy of Prime Video. For those seeking an alternative to the MCU, Prime Video has two offerings of the live-action and animated variety that take the superhero genre into R-rated territory where the hands of the god-like figures get dirty, bloodied and severed. “The Boys is about the intersection of celebrity and politics using superheroes,” states Stephan Fleet, VFX Supervisor on The Boys. “Sometimes I see the news and I don’t even know we can write to catch up to it! But we try. Invincible is an intense look at an alternate DC Universe that has more grit to the superhero side of it all. On one hand, I was jealous watching Season 1 of Invincible because in animation you can do things that you can’t do in real life on a budget.” Season 4 does not tone down the blood, gore and body count. Fleet notes, “The writers almost have this dialogue with us. Sometimes, they’ll write in the script, ‘And Fleet will come up with a cool visual effect for how to kill this person.’ Or, ‘Chhiu, our fight coordinator, will make an awesome fight.’ It is a frequent topic of conversation. We’re constantly trying to be inventive and create new ways to kill people!” When Splinter (Rob Benedict) splits in two, the cloning effect was inspired by cellular mitosis. “The writers almost have this dialogue with us. Sometimes, they’ll write in the script, ‘And Fleet will come up with a cool visual effect for how to kill this person.’ Or, ‘Chhiu, our fight coordinator, will make an awesome fight.’ It is a frequent topic of conversation. We’re constantly trying to be inventive and create new ways to kill people!” —Stephan Fleet, VFX Supervisor A total of 1,600 visual effects shots were created for the eight episodes by ILM, Pixomondo, MPC Toronto, Spin VFX, DNEG, Untold Studios, Luma Pictures and Rocket Science VFX. Previs was a critical part of the process. “We have John Griffith [Previs Director], who owns a small company called CNCPT out of Texas, and he does wonderful Unreal Engine level previs,” Fleet remarks. “On set, we have a cartoon of what is going to be done, and you’ll be amazed, specifically for action and heavy visual effects stuff, how close those shots are to the previs when we finish.” Founding Director of Federal Bureau of Superhuman Affairs, Victoria Neuman, literally gets ripped in half by two tendrils coming out of Compound V-enhanced Billy Butcher, the leader of superhero resistance group The Boys. “The word that we like to use on this show is ‘grounded,’ and I like to say ‘grounded’ with an asterisk in this day and age because we’re grounded until we get to killing people in the craziest ways. In this case, having someone floating in the air and being ripped in half by two tendrils was all CG.” Multiple plates were shot to enable Simon Pegg to phase through the actor laying in a hospital bed. Testing can get rather elaborate. “For that end scene with Butcher’s tendrils, the room was two stories, and we were able to put the camera up high along with a bunch of blood cannons,” Fleet recalls. “When the body rips in half and explodes, there is a practical component. We rained down a bunch of real blood and guts right in front of Huey. It’s a known joke that we like to douse Jack Quaid with blood as much as possible! In this case, the special effects team led by Hudson Kenny needed to test it the day before, and I said, “I’ll be the guinea pig for the test.’ They covered the whole place with plastic like it was a Dexter kill room because you don’t want to destroy the set. I’m standing there in a white hazmat suit with goggles on, covered from head to toe in plastic and waiting as they’re tweaking all of these things. It sounds like World War II going on. They’re on walkie talkies to each other, and then all of a sudden, it’s ‘Five, four, three, two, one…’  And I get exploded with blood. I wanted to see what it was like, and it’s intense.” “On set, we have a cartoon of what is going to be done, and you’ll be amazed, specifically for action and heavy visual effects stuff, how close those shots are to the previs when we finish.” —Stephan Fleet, VFX Supervisor The Deep has a love affair with an octopus called Ambrosius, voiced by Tilda Swinton. “It’s implied bestiality!” Fleet laughs. “I would call it more of a romance. What was fun from my perspective is that I knew what the look was going to be [from Season 3], so then it’s about putting in the details and the animation. One of the instincts that you always have when you’re making a sea creature that talks to a human [is] you tend to want to give it human gestures and eyebrows. Erik Kripke [Creator, Executive Producer, Showrunner, Director, Writer] said, ‘No. We have to find things that an octopus could do that conveys the same emotion.’ That’s when ideas came in, such as putting a little The Deep toy inside the water tank. When Ambrosius is trying to have an intimate moment or connect with him, she can wrap a tentacle around that. My favorite experience doing Ambrosius was when The Deep is reading poetry to her on a bed. CG creatures touching humans is one of the more complicated things to do and make look real. Ambrosius’ tentacles reach for his arm, and it becomes an intimate moment. More than touching the skin, displacing the bedsheet as Ambrosius moved ended up becoming a lot of CG, and we had to go back and forth a few times to get that looking right; that turned out to be tricky.” A building is replaced by a massive crowd attending a rally being held by Homelander. In a twisted form of sexual foreplay, Sister Sage has The Deep perform a transorbital lobotomy on her. “Thank you, Amazon for selling lobotomy tools as novelty items!” Fleet chuckles. “We filmed it with a lobotomy tool on set. There is a lot of safety involved in doing something like that. Obviously, you don’t want to put any performer in any situation where they come close to putting anything real near their eye. We created this half lobotomy tool and did this complicated split screen with the lobotomy tool on a teeter totter. The Deep was [acting in a certain way] in one shot and Sister Sage reacted in the other shot. To marry the two ended up being a lot of CG work. Then there are these close-ups which are full CG. I always keep a dummy head that is painted gray that I use all of the time for reference. In macrophotography I filmed this lobotomy tool going right into the eye area. I did that because the tool is chrome, so it’s reflective and has ridges. It has an interesting reflective property. I was able to see how and what part of the human eye reflects onto the tool. A lot of that shot became about realistic reflections and lighting on the tool. Then heavy CG for displacing the eye and pushing the lobotomy tool into it. That was one of the more complicated sequences that we had to achieve.” In order to create an intimate moment between Ambrosius and The Deep, a toy version of the superhero was placed inside of the water tank that she could wrap a tentacle around. “The word that we like to use on this show is ‘grounded,’ and I like to say ‘grounded’ with an asterisk in this day and age because we’re grounded until we get to killing people in the craziest ways. In this case, having someone floating in the air and being ripped in half by two tendrils was all CG.” —Stephan Fleet, VFX Supervisor Sheep and chickens embark on a violent rampage courtesy of Compound V with the latter piercing the chest of a bodyguard belonging to Victoria Neuman. “Weirdly, that was one of our more traditional shots,’ Fleet states. “What is fun about that one is I asked for real chickens as reference. The chicken flying through his chest is real. It’s our chicken wrangler in green suit gently tossing a chicken. We blended two real plates together with some CG in the middle.” A connection was made with a sci-fi classic. “The sheep kill this bull, and we shot it is in this narrow corridor of fencing. When they run, I always equated it as the Trench Run in Star Wars and looked at the sheep as TIE fighters or X-wings coming at them.” The scene was one of the scarier moments for the visual effects team. Fleet explains, “When I read the script, I thought this could be the moment where we jump the shark. For the shots where the sheep are still and scream to the camera, Untold Studios did a bunch of R&D and came up with baboon teeth. I tried to keep anything real as much as possible, but, obviously, when sheep are flying, they have to be CG. I call it the Battlestar Galactica theory, where I like to shake the camera, overshoot shots and make it sloppy when they’re in the air so you can add motion blur. Comedy also helps sell visual effects.” The sheep injected with Compound V develop the ability to fly and were shot in an imperfect manner to help ground the scenes. Once injected with Compound V, Hugh Campbell Sr. (Simon Pegg) develops the ability to phase through objects, including human beings. “We called it the Bro-nut because his name in the script is Wall Street Bro,” Fleet notes. “That was a complicated motion control shot, repeating the move over and over again. We had to shoot multiple plates of Simon Pegg and the guy in the bed. Special effects and prosthetics created a dummy guy with a hole in his chest with practical blood dripping down. It was meshing it together and getting the timing right in post. On top of that, there was the CG blood immediately around Simon Pegg.” The phasing effect had to avoid appearing as a dissolve. “I had this idea of doing high-frequency vibration on the X axis loosely based on how The Flash vibrates through walls. You want everything to have a loose motivation that then helps trigger the visuals. We tried not to overcomplicate that because, ultimately, you want something like that to be quick. If you spend too much time on phasing, it can look cheesy. In our case, it was a lot of false walls. Simon Pegg is running into a greenscreen hole which we plug in with a wall or coming out of one. I went off the actor’s action, and we added a light opacity mix with some X-axis shake.” Providing a different twist to the fights was the replacement of spurting blood with photoreal rubber duckies during a drug-induced hallucination. Homelander (Anthony Starr) breaks a mirror which emphasizes his multiple personality disorder. “The original plan was that special effects was going to pre-break a mirror, and we were going to shoot Anthony Starr moving his head doing all of the performances in the different parts of the mirror,” Fleet reveals. “This was all based on a photo that my ex-brother-in-law sent me. He was walking down a street in Glendale, California, came across a broken mirror that someone had thrown out, and took a photo of himself where he had five heads in the mirror. We get there on the day, and I’m realizing that this is really complicated. Anthony has to do these five different performances, and we have to deal with infinite mirrors. At the last minute, I said, ‘We have to do this on a clean mirror.’ We did it on a clear mirror and gave Anthony different eyelines. The mirror break was all done in post, and we were able to cheat his head slightly and art-direct where the break crosses his chin. Editorial was able to do split screens for the timing of the dialogue.” “For the shots where the sheep are still and scream to the camera, Untold Studios did a bunch of R&D and came up with baboon teeth. I tried to keep anything real as much as possible, but, obviously, when sheep are flying, they have to be CG. I call it the Battlestar Galactica theory, where I like to shake the camera, overshoot shots and make it sloppy when they’re in the air so you can add motion blur. Comedy also helps sell visual effects.” —Stephan Fleet, VFX Supervisor Initially, the plan was to use a practical mirror, but creating a digital version proved to be the more effective solution. A different spin on the bloodbath occurs during a fight when a drugged Frenchie (Tomer Capone) hallucinates as Kimiko Miyashiro (Karen Fukuhara) goes on a killing spree. “We went back and forth with a lot of different concepts for what this hallucination would be,” Fleet remarks. “When we filmed it, we landed on Frenchie having a synesthesia moment where he’s seeing a lot of abstract colors flying in the air. We started getting into that in post and it wasn’t working. We went back to the rubber duckies, which goes back to the story of him in the bathtub. What’s in the bathtub? Rubber duckies, bubbles and water. There was a lot of physics and logic required to figure out how these rubber duckies could float out of someone’s neck. We decided on bubbles when Kimiko hits people’s heads. At one point, we had water when she got shot, but it wasn’t working, so we killed it. We probably did about 100 different versions. We got really detailed with our rubber duckie modeling because we didn’t want it to look cartoony. That took a long time.” Ambrosius, voiced by Tilda Swinton, gets a lot more screentime in Season 4. When Splinter (Rob Benedict) splits in two was achieved heavily in CG. “Erik threw out the words ‘cellular mitosis’ early on as something he wanted to use,” Fleet states. “We shot Rob Benedict on a greenscreen doing all of the different performances for the clones that pop out. It was a crazy amount of CG work with Houdini and particle and skin effects. We previs’d the sequence so we had specific actions. One clone comes out to the right and the other pulls backwards.” What tends to go unnoticed by many is Splinter’s clones setting up for a press conference being held by Firecracker (Valorie Curry). “It’s funny how no one brings up the 22-hour motion control shot that we had to do with Splinter on the stage, which was the most complicated shot!” Fleet observes. “We have this sweeping long shot that brings you into the room and follows Splinter as he carries a container to the stage and hands it off to a clone, and then you reveal five more of them interweaving each other and interacting with all of these objects. It’s like a minute-long dance. First off, you have to choreograph it. We previs’d it, but then you need to get people to do it. We hired dancers and put different colored armbands on them. The camera is like another performer, and a metronome is going, which enables you to find a pace. That took about eight hours of rehearsal. Then Rob has to watch each one of their performances and mimic it to the beat. When he is handing off a box of cables, it’s to a double who is going to have to be erased and be him on the other side. They have to be almost perfect in their timing and lineup in order to take it over in visual effects and make it work.”

En los últimos años, la impresión 3D de hormigón se ha venido consolidando como una tecnología legítima dentro de la industria de la construcción. Esta técnica permite producir edificaciones de forma más rápida y rentable, por lo que los expertosR