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Introducing PointZero—a 3D world model pre-trained without robots. Dexterous manipulation requires understanding diverse 3D dynamics, but current approaches rely on expensive robot data. So how can we pre-train a 3D dynamics model without it? PointZero introduces a simple idea: learning to complete 3D point tracks yields transferable 3D dynamics!... show more

Bardienus Duisterhof

1,398 subscribers

27,011 views • 3 days ago •via X (Twitter)

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14 Comments

Bardienus Duisterhof3 days ago

Our approach is simple: given an RGB-D observation and only 1-3 tracks, PointZero denoises point tracks for all observed points. Sparse tracks constrain possible outcomes, but leave properties such as stiffness and joint structure ambiguous. 2/6

Bardienus Duisterhof3 days ago

Trained on just 2.9M frames of diverse synthetic dynamics, PointZero produces plausible zero-shot predictions of real-world dynamics. We test it on deformable, articulated, and rigid objects. 3/6

Bardienus Duisterhof3 days ago

We post-train PointZero for two downstream applications. First, action-conditioned 3D dynamics: instead of point tracks, we condition on the robot’s end-effector state. PointZero outperforms the task specialists we evaluate. 4/6

Bardienus Duisterhof3 days ago

Next, we turn PointZero into an MM-DiT to predict robot actions through imitation learning. It matches or outperforms existing behavior cloning methods. We also compare pre-trained PointZero with training from scratch: pre-training improves performance in both downstream applications. 5/6

Bardienus Duisterhof3 days ago

Thanks to awesome co-authors @kaiwynd @Adamjhung @bowenwen_me @BirchfieldStan @YunzhuLiYZ @RamananDeva @jeff_ichnowski The paper is out now; the code and dataset will follow shortly. Website: arXiv: 6/6

Homanga Bharadhwaj3 days ago

I like this! @bardienus

Bardienus Duisterhof3 days ago

Thanks prof @mangahomanga !

Andrea Miele3 days ago

bro is on rampage

Bardienus Duisterhof3 days ago

Lol release week 😄

Andrea Miele3 days ago

Hahah do you have more ? 👀

Bardienus Duisterhof3 days ago

Just this one:

Andrea Miele3 days ago

😂😂😂 my man

v98cheox.club3 days ago

wild scaling

Muhammad Ahmed3 days ago

One to three tracks can leave several motions plausible, especially with hidden joints or stiffness. Preserving that uncertainty seems important if a robot is going to choose its next action from the prediction.

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What if we can simulate an *interactive 3D world*, from a single image, in the wild, in real time? Introducing PointWorld-1B: a large pre-trained 3D world model that predicts env dynamics given RGB-D capture and robot actions. 🌐 from Stanford University NVIDIA

What if we can simulate an interactive 3D world, from a single image, in the wild, in real time? Introducing PointWorld-1B: a large pre-trained 3D world model that predicts env dynamics given RGB-D capture and robot actions. 🌐 from Stanford University NVIDIA

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41,747 views • 2 years ago

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123,517 views • 1 year ago

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AK

38,571 views • 3 years ago

Instead of choosing between training a world model and training a language conditioned robot policy, why not do both? LDA-1B s a new foundation model that is trained on 30,000 hours of human and robot interaction data. Part of the secret is that LDA-1B jointly learns forward dynamics, action prediction, and visual forecasting, all in a structured DINO latent space which avoids the pitfalls of redundant pixel-level prediction which isn’t necessarily aligned robot action. This approach works on both dexterous hands and simple robot grippers; it also generalizes across objects, tasks, and scenes. Jiangran Lyu joins us to explain. Learn more on Episode 98 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton!

Instead of choosing between training a world model and training a language conditioned robot policy, why not do both? LDA-1B s a new foundation model that is trained on 30,000 hours of human and robot interaction data. Part of the secret is that LDA-1B jointly learns forward dynamics, action prediction, and visual forecasting, all in a structured DINO latent space which avoids the pitfalls of redundant pixel-level prediction which isn’t necessarily aligned robot action. This approach works on both dexterous hands and simple robot grippers; it also generalizes across objects, tasks, and scenes. Jiangran Lyu joins us to explain. Learn more on Episode 98 of RoboPapers, with Michael Cho - Rbt/Acc and Chris Paxton!

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16,335 views • 27 days ago

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1,242,247 views • 2 years ago

V3D Video Diffusion Models are Effective 3D Generators Automatic 3D generation has recently attracted widespread attention. Recent methods have greatly accelerated the generation speed, but usually produce less-detailed objects due to limited model capacity or 3D data. Motivated by recent advancements in video diffusion models, we introduce V3D, which leverages the world simulation capacity of pre-trained video diffusion models to facilitate 3D generation. To fully unleash the potential of video diffusion to perceive the 3D world, we further introduce geometrical consistency prior and extend the video diffusion model to a multi-view consistent 3D generator. Benefiting from this, the state-of-the-art video diffusion model could be fine-tuned to generate 360degree orbit frames surrounding an object given a single image. With our tailored reconstruction pipelines, we can generate high-quality meshes or 3D Gaussians within 3 minutes. Furthermore, our method can be extended to scene-level novel view synthesis, achieving precise control over the camera path with sparse input views. Extensive experiments demonstrate the superior performance of the proposed approach, especially in terms of generation quality and multi-view consistency

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AK

31,997 views • 2 years ago

We added Quake3-style multiplayer to our 3D world generator and it changes the game, literally 🔥🔥🔥 Runs directly on our model output: fast movement, arena combat, and every single match plays out on a generated world. Stay tuned for public release and play yourself!

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SpAItial AI

16,229 views • 3 months ago

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Felix Heide

19,625 views • 5 months ago