Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling

TL;DR

This paper presents a novel framework that learns object dynamics directly from multi-view RGB videos using 3D Gaussian representations and graph neural networks, enabling accurate action-conditioned video prediction and robotic manipulation planning.

Contribution

It introduces a 3D Gaussian-based neural dynamics model that explicitly incorporates robot actions and scene dynamics from multi-view videos, improving prediction and planning in robotic applications.

Findings

Successfully models complex deformable object dynamics.

Enables accurate action-conditioned future state prediction.

Demonstrates effectiveness on various deformable materials.

Abstract

Videos of robots interacting with objects encode rich information about the objects' dynamics. However, existing video prediction approaches typically do not explicitly account for the 3D information from videos, such as robot actions and objects' 3D states, limiting their use in real-world robotic applications. In this work, we introduce a framework to learn object dynamics directly from multi-view RGB videos by explicitly considering the robot's action trajectories and their effects on scene dynamics. We utilize the 3D Gaussian representation of 3D Gaussian Splatting (3DGS) to train a particle-based dynamics model using Graph Neural Networks. This model operates on sparse control particles downsampled from the densely tracked 3D Gaussian reconstructions. By learning the neural dynamics model on offline robot interaction data, our method can predict object motions under varying initial configurations and unseen robot actions. The 3D transformations of Gaussians can be interpolated from the motions of control particles, enabling the rendering of predicted future object states and achieving action-conditioned video prediction. The dynamics model can also be applied to model-based planning frameworks for object manipulation tasks. We conduct experiments on various kinds of deformable materials, including ropes, clothes, and stuffed animals, demonstrating our framework's ability to model complex shapes and dynamics. Our project page is available at https://gs-dynamics.github.io.