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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 просмотров • 3 дней назад •via X (Twitter)

Образование Наука и технологии

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Комментарии: 14

Фото профиля Bardienus Duisterhof

Bardienus Duisterhof3 дней назад

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

Фото профиля Bardienus Duisterhof

Bardienus Duisterhof3 дней назад

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

Фото профиля Bardienus Duisterhof

Bardienus Duisterhof3 дней назад

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

Фото профиля Bardienus Duisterhof

Bardienus Duisterhof3 дней назад

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

Фото профиля Bardienus Duisterhof

Bardienus Duisterhof3 дней назад

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

Фото профиля Homanga Bharadhwaj

Homanga Bharadhwaj3 дней назад

I like this! @bardienus

Фото профиля Bardienus Duisterhof

Bardienus Duisterhof3 дней назад

Thanks prof @mangahomanga !

Фото профиля Andrea Miele

Andrea Miele3 дней назад

bro is on rampage

Фото профиля Bardienus Duisterhof

Bardienus Duisterhof3 дней назад

Lol release week 😄

Фото профиля Andrea Miele

Andrea Miele3 дней назад

Hahah do you have more ? 👀

Фото профиля Bardienus Duisterhof

Bardienus Duisterhof3 дней назад

Just this one:

Фото профиля Andrea Miele

Andrea Miele3 дней назад

😂😂😂 my man

Фото профиля v98cheox.club

v98cheox.club3 дней назад

wild scaling

Фото профиля Muhammad Ahmed

Muhammad Ahmed3 дней назад

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

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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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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 просмотров • 2 лет назад

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 просмотров • 3 месяцев назад

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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 просмотров • 5 месяцев назад