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NVIDIA Graphics Research has presented 22 technical breakthroughs at SIGGRAPH 2026, advancing Simulation and Physical AI.
Physical AI Sees Life-Like Simulation With NVIDIA’s Latest Graphics Breakthroughs While ArtFixer Turns Messy 3D Captures Into A Pristine Virtual Scene
At SIGGRAPH, NVIDIA’s Graphics Research has published 22 technical papers with the aim of advancing Physical AI and Simulation through leveraging its real-time and AI-assisted technologies.
NVIDIA is using AI to build real-time systems to generate physically accurate virtual worlds for both virtual purposes, for enjoying inside the virtual world, but also to build AI systems and real things inside the real world. All of the research is grounded in 3D with physics, and it’s all directable by creators. There are two notable breakthroughs I’d like to highlight.
Whether the output is a game, film, robot or factory digital twin, the goal is the same: expand the canvas of creativity with AI-generated worlds that are grounded in 3D, governed by physics and directed by creators.
The first of these is called MotionBricks.You can think of it kind of like a foundation model that generates lifelike character motion that’s very seamless and smooth, but it does it in real time. This can drive both an animated character that’s inside a virtual world or in a virtual experience, and it can generate the motion for a real humanoid robot. NVIDIA states that MotionBricks has been trained on over 350,000 motion clips running at game-engine speeds. This allows creators direct control of character movements. This technology has already been applied to Unitree’s G1 humanoid robot.
The next technology highlighted by NVIDIA is called Artifixer. This is a model that can take a rough, very incomplete, and noisy 3D scan of the real world and turn it into a clean and complete 3D scene. It can even fill in areas that have gaps. And it does so in a way that delivers pristine quality renders. NVIDIA wants us to look at Artfixer as a cleaner that takes a messy, noisy, incomplete Gaussian splat and makes it clean and complete.
Next up is GPC, which is a framework for training generative controllers on a large-scale motion dataset. GPC will be the start of a foundation model for motor control.
It’s available and free to use in that way. We also take the output of our research and integrate it into our software offerings, into our libraries like the Omniverse libraries, but we also work with our partners, our developer partners, to help them integrate the best ideas that come from our research into their tools and offerings.
NVIDIA states that all of its technical breakthroughs presented at SIGGRAPH include open- technologies mentioned in the papers. These will be made available on GitHub and other repositories, and developers will be able to use them freely
About the author: A Software Engineer by training and a PC enthusiast by passion, Hassan Mujtaba serves as Wccftech’s Senior Editor for hardware section. With years of experience in the industry, he specializes in deep-dive technical analysis of next-generation CPU and GPU architectures, motherboards, and cooling solutions. His work involves not only breaking news on upcoming technologies but also extensive hands-on reviews and benchmarking.
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