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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 次观看 • 3 天前 •via X (Twitter)

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14 条评论

Bardienus Duisterhof 的头像

Bardienus Duisterhof3 天前

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 Duisterhof 的头像

Bardienus Duisterhof3 天前

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 Duisterhof 的头像

Bardienus Duisterhof3 天前

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 Duisterhof 的头像

Bardienus Duisterhof3 天前

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 Duisterhof 的头像

Bardienus Duisterhof3 天前

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 Bharadhwaj 的头像

Homanga Bharadhwaj3 天前

I like this! @bardienus

Bardienus Duisterhof 的头像

Bardienus Duisterhof3 天前

Thanks prof @mangahomanga !

Andrea Miele 的头像

Andrea Miele3 天前

bro is on rampage

Bardienus Duisterhof 的头像

Bardienus Duisterhof3 天前

Lol release week 😄

Andrea Miele 的头像

Andrea Miele3 天前

Hahah do you have more ? 👀

Bardienus Duisterhof 的头像

Bardienus Duisterhof3 天前

Just this one:

Andrea Miele 的头像

Andrea Miele3 天前

😂😂😂 my man

v98cheox.club 的头像

v98cheox.club3 天前

wild scaling

Muhammad Ahmed 的头像

Muhammad Ahmed3 天前

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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Robot Learning needs 4D world models! Robot Learning needs 4D world models! Robot Learning needs 4D world models! We introduce TesserAct, a 4D embodied world model that can simulate how agents interact with the 3D world over time! We achieve this by simply extending a pre-trained 2D video generation model to jointly predict RGB, depth, and surface normals. It enables: 1️⃣ Much better policy learning in the wild 2️⃣ Temporal + spatial coherence in 4D dynamic prediction 3️⃣ Novel view synthesis for embodied scenes Code: Paper Link: Project page:

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43,265 次观看 • 1 年前

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Meet MapAnything – a transformer that directly regresses factored metric 3D scene geometry (from images, calibration, poses, or depth) in an end-to-end way. No pipelines, no extra stages. Just 3D geometry & cameras, straight from any type of input, delivering new state-of-the-art results 🚀 One universal model enables SoTA for: 🔥 Mono Depth Estimation 🔥 Multi-View SfM 🔥 Multi-View Stereo 🔥 Depth Completion 🔥 Registration … and many more possibilities! – plus everything is metric 🎯 We release code for data processing, training, benchmarking & ablations – everything Apache 2.0! Details & Links 👇

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123,517 次观看 • 1 年前

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Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitives paper page: Given a set of calibrated images of a scene, we present an approach that produces a simple, compact, and actionable 3D world representation by means of 3D primitives. While many approaches focus on recovering high-fidelity 3D scenes, we focus on parsing a scene into mid-level 3D representations made of a small set of textured primitives. Such representations are interpretable, easy to manipulate and suited for physics-based simulations. Moreover, unlike existing primitive decomposition methods that rely on 3D input data, our approach operates directly on images through differentiable rendering. Specifically, we model primitives as textured superquadric meshes and optimize their parameters from scratch with an image rendering loss. We highlight the importance of modeling transparency for each primitive, which is critical for optimization and also enables handling varying numbers of primitives. We show that the resulting textured primitives faithfully reconstruct the input images and accurately model the visible 3D points, while providing amodal shape completions of unseen object regions. We compare our approach to the state of the art on diverse scenes from DTU, and demonstrate its robustness on real-life captures from BlendedMVS and Nerfstudio. We also showcase how our results can be used to effortlessly edit a scene or perform physical simulations.

AK

38,571 次观看 • 3 年前

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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!

RoboPapers

16,335 次观看 • 27 天前

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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

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

AK

31,997 次观看 • 2 年前

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!

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!

SpAItial AI

16,229 次观看 • 3 个月前

WorldFlow3D: Unbounded 3D World Generation 🌍 by Flow Through Hierarchical Distributions, without VAEs ! We reformulate 3D generation as flowing through sequentially finer 3D distributions, cutting training time by more than half ⏱️ compared to existing approaches! Vectorized map layouts provide full scene controllability 🗺️, and a novel flow-field alignment process enables causally coherent, spatially unbounded generation 🌍. This generative method generalizes across both real and synthetic data distributions! Project: Project led by Amogh Joshi and Julian Ost — will be super fun to build on this! 🔥

WorldFlow3D: Unbounded 3D World Generation 🌍 by Flow Through Hierarchical Distributions, without VAEs ! We reformulate 3D generation as flowing through sequentially finer 3D distributions, cutting training time by more than half ⏱️ compared to existing approaches! Vectorized map layouts provide full scene controllability 🗺️, and a novel flow-field alignment process enables causally coherent, spatially unbounded generation 🌍. This generative method generalizes across both real and synthetic data distributions! Project: Project led by Amogh Joshi and Julian Ost — will be super fun to build on this! 🔥

Felix Heide

19,625 次观看 • 5 个月前