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Can we learn a 3D world model that predicts object dynamics directly from videos? Introducing Particle-Grid Neural Dynamics: a learning-based simulator for deformable objects that trains from real-world videos. Website: ArXiv: Code: Demo: To appear at #RSS2025

Kaifeng Zhang

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45,946 görüntüleme • 1 yıl önce •via X (Twitter)

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Kaifeng Zhang profil fotoğrafı

Kaifeng Zhang1 yıl önce

Modeling ropes, cloth, bags, etc. is hard because of their complex physics and partial observability. Classical simulators struggle to construct exact digital twins from real observations. We overcome these challenges by learning neural dynamics directly from videos.

Kaifeng Zhang profil fotoğrafı

Kaifeng Zhang1 yıl önce

Our particle-based neural dynamics model represents objects as dense 3D particles and predicts their next-step velocities to simulate object dynamics. It features three stages: particle encoding, grid-velocity editing, and grid-to-particle velocity transfer.

Kaifeng Zhang profil fotoğrafı

Kaifeng Zhang1 yıl önce

Trained with videos including robot–object interactions under self-supervion, PGND can model diverse deformable objects—including ropes, cloth, stuffed animals, and paper bags—using <20 minutes of data per object.

Kaifeng Zhang profil fotoğrafı

Kaifeng Zhang1 yıl önce

PGND becomes a 3D action-conditioned video generator when 3D Gaussian Splatting is plugged in. It aligns better with ground truth, producing visually more realistic deformations than the baseline.

Kaifeng Zhang profil fotoğrafı

Kaifeng Zhang1 yıl önce

PGND can also act as a photorealistic deformable-object simulator with a complete scan of the scene. Given only a static reconstruction, we simulate the segmented object’s motion with a sequence of robot actions (red arrows).

Kaifeng Zhang profil fotoğrafı

Kaifeng Zhang1 yıl önce

Finally, PGND serves as a 3D world model within Model Predictive Control. It guides dual-arm cloth lifting, rope shaping, box closing, and plush-toy relocation, achieving fast convergence to target configurations.

Kaifeng Zhang profil fotoğrafı

Kaifeng Zhang1 yıl önce

This work is a close collaboration between Columbia University @ColumbiaCompSci and University of Illinois Urbana-Champaign @siebelschool. Huge thanks to my co-authors: @YunzhuLiYZ, Kris Hauser, @BaoyuLi6 !

Carlos DP profil fotoğrafı

Carlos DP1 yıl önce

I love this, and that you made a hf space demo

Hongyu Li profil fotoğrafı

Hongyu Li1 yıl önce

This is an exciting work. Congrats!!

Kaifeng Zhang profil fotoğrafı

Kaifeng Zhang1 yıl önce

Thank you Hongyu!

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