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Today, we're introducing SimFoundry, our real2sim2real framework at NVIDIA GEAR that automatically turns real-world scenes into simulation-ready worlds from a single image or video. Website: Paper: This work marks a major step for our team toward leveraging simulations and synthetic data for foundation model training and systematic policy evaluation... show more

Yuke Zhu

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80,828 görüntüleme • 3 ay önce •via X (Twitter)

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Aetheris_Consulting 🇺🇸 profil fotoğrafı

Aetheris_Consulting 🇺🇸3 ay önce

Yuke awesome work here!!! Can you consider making a plug in for @UnrealEngine @unity @godotengine ? That was through a harness LLM can do physics computations paired with real2sim2real to do a number of things. This is such great work pls keep it up!!! 👍

Arthur Petron profil fotoğrafı

Arthur Petron3 ay önce

How well do dynamics work? Like, if I drop the tennis ball from 0.5m in real does it bounce the same in sim?

Weijie Wang profil fotoğrafı

Weijie Wang2 ay önce

Another antidote to hype—real frontier work with paper and open-source pledge, verifiable author; judge it by the arXiv paper, not the summary.

Sitarama Chekuri profil fotoğrafı

Sitarama Chekuri3 ay önce

Looks amazing. Are the objects physics ready and interactive?

Mikhail profil fotoğrafı

Mikhail3 ay önce

thanks habibi

Ferbin profil fotoğrafı

Ferbin3 ay önce

Robot training always gets stuck building training environments. Automating that from photos changes the whole game.

Efstratios Gavves profil fotoğrafı

Efstratios Gavves3 ay önce

@yukez Great work! I think it would be fair and nice to cite our DreMa work that was the first to introduce in ICLR 2025 the paradigm:

just10101 profil fotoğrafı

just101012 ay önce

Shameless @danfei_xu Stop stealing from students/ interviewees Shame on @gtcomputing

EB1A Experts profil fotoğrafı

EB1A Experts3 ay önce

Impressive work. Bridging real-world scenes and simulation-ready environments from minimal input is an exciting step toward more scalable robotics and foundation model development. Looking forward to seeing the open-source release.

Larry Panozzo profil fotoğrafı

Larry Panozzo3 ay önce

Wow, from one video from one lens with depth estimation…amazing. Most applications would use stereo I imagine? Results could be even better then, with a different version of this framework?

Zavian Pokharkar profil fotoğrafı

Zavian Pokharkar2 ay önce

Yuke

Adel Dennaoui profil fotoğrafı

Adel Dennaoui2 ay önce

When will the code be released? :)

AI Mastery Guide profil fotoğrafı

AI Mastery Guide3 ay önce

Real2sim2real from a single image is huge for robotics training data, excited to see this open-sourced.

Kavya profil fotoğrafı

Kavya1 ay önce

@yukez, when will you be releasing the code? Would love to try this out!

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Most AI world models can generate beautiful scenes. Keeping those scenes alive for an hour without falling apart is the real challenge. That's what caught my attention about LingBot-World 2.0 (LingBot-World-Infinity) from Robbyant Instead of chasing longer videos, it focuses on something much harder: persistent, interactive worlds that stay coherent while you explore. A few highlights: • Generates worlds from a single frame and continuously responds to live user actions through a causal world model. • Streams stable 720p at 60 FPS in real time. The team reports a continuous 60 minute stress test across 20 different scenarios with no noticeable visual degradation. • Uses a Brain-Cerebellum co-simulation framework where a VLM plans events while the video model turns them into consistent world evolution. • Pilot and Director Agents help drive character behavior and introduce new objects and events. • Open sourced with a 14B flagship model, while the paper also describes a lightweight 1.3B version for a single consumer GPU. There is also an online interactive demo. The biggest takeaway? We're moving beyond AI that generates clips. We're getting closer to AI that generates living, evolving worlds you can actually interact with. And that feels like a much bigger shift than another jump in video quality. Explore more: 💻 Github: 🤗 Weights: 🌐 Website-with videos you can use : 🎮 Try it online: #Robbyant #LingBot #WorldModel #EmbodiedAI #OpenSource #Robotics #ad

Alif Khan

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Introducing SimReadyGen. Agentic simulation generation for Physical AI. From a text prompt to physically accurate SimReady assets at scale. Robot training demands physically accurate environments at a scale traditional asset creation can't match. SimReadyGen closes that gap. Every generated asset carries measured physics, not estimates. Behind it is SimReady Foundry, our measured-physics pipeline: we measure how real objects behave — contact, friction, dynamics — and turn those measurements into ground-truth simulation parameters. Built on OpenUSD and integrated with NVIDIA Robotics Omniverse Libraries, SimReadyGen moves directly into Isaac Sim and Isaac Lab workflows. Try it today: Read the announcement: #PhysicalAI #Robotics #Simulation #OpenUSD

Introducing SimReadyGen. Agentic simulation generation for Physical AI. From a text prompt to physically accurate SimReady assets at scale. Robot training demands physically accurate environments at a scale traditional asset creation can't match. SimReadyGen closes that gap. Every generated asset carries measured physics, not estimates. Behind it is SimReady Foundry, our measured-physics pipeline: we measure how real objects behave — contact, friction, dynamics — and turn those measurements into ground-truth simulation parameters. Built on OpenUSD and integrated with NVIDIA Robotics Omniverse Libraries, SimReadyGen moves directly into Isaac Sim and Isaac Lab workflows. Try it today: Read the announcement: #PhysicalAI #Robotics #Simulation #OpenUSD

Lightwheel

12,221 görüntüleme • 2 ay önce

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We’re excited to share DiT4DiT, an end-to-end Video-Action Model for robot learning that unifies a video Diffusion Transformer and an action Diffusion Transformer in a single cascaded framework. By leveraging the rich spatiotemporal and physical dynamics learned through video generation, rather than static image-text priors, DiT4DiT achieves state-of-the-art results on LIBERO (98.6%) and RoboCasa GR1 (50.8%) with far less training data, delivering over 10× better sample efficiency and up to 7× faster convergence. Real-world deployment on a humanoid robot further shows robust generalization. We believe this is a step toward making video generation a powerful backbone for robot policy learning. This work builds upon the brilliant foundations laid by Nvidia's GR00T and Cosmos. Project: Paper: Code: Coming soon. In the meantime, you can ask your coding agent to reproduce the method based on GR00T/Cosmos.

Shuo Yang

31,872 görüntüleme • 6 ay önce

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📢 Announcing one of the most exciting works from us this year on scalable robot policy evaluation through real-to-sim transfer, moving toward a scalable evaluation engine with structured world models that capture the appearance, geometry, and dynamics of environments involving deformable objects. 🤖 Evaluation remains one of the biggest bottlenecks in building general-purpose robots. Today, robots are still evaluated only in the real world, which is orders of magnitude slower than the development of language agents. We propose a new framework where simulation performance strongly correlates with the real world (r > 0.9), even for deformable objects. The key difference from existing work lies in the correlation between simulation and reality: if a robot model performs better in the digital world, does it also perform better in the real world? This question has long made people hesitant about simulation-based evaluation — especially for deformable objects. We are changing that. Our pipeline achieves effective real-to-sim transfer, establishing state-of-the-art correlation between simulation and reality for deformable object manipulation. It provides a scalable and reproducible evaluation engine for robot learning. 🌐

Yunzhu Li

39,960 görüntüleme • 11 ay önce

introducing: the open ecosystem for physical intelligence. a unified foundation where real-time, verified data from machines powers intelligent systems that sense, reason, and act in the real world. today marks a new chapter for IoTeX. visit the new website: since 2017, we've connected millions of smart devices to blockchain and helped launch the global DePIN movement. we built a high-performance L1 for machines, then modular infrastructure for DePINs. now, we're expanding into AI. this is chapter 3 for IoTeX. and the next evolution for AI.

introducing: the open ecosystem for physical intelligence. a unified foundation where real-time, verified data from machines powers intelligent systems that sense, reason, and act in the real world. today marks a new chapter for IoTeX. visit the new website: since 2017, we've connected millions of smart devices to blockchain and helped launch the global DePIN movement. we built a high-performance L1 for machines, then modular infrastructure for DePINs. now, we're expanding into AI. this is chapter 3 for IoTeX. and the next evolution for AI.

IoTeX

856,542 görüntüleme • 1 yıl önce

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

📢 Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation Got only one or a few images and wondering if recovering the 3D environment is a reconstruction or generation problem? Why not do it with a generative reconstruction model! We show that a camera-conditioned video diffusion model can be transformed into a generative reconstruction model that directly outputs a high-quality 3D Gaussian Splatting representation through self-distillation, without requiring real-world training data. Check out our results in the video (wait for dynamic scenes in the second half!) : Project Page: Code and Models: Paper:

Sherwin Bahmani

66,897 görüntüleme • 1 yıl önce