Dataset Card for Goal Force
We train a video model which allows users to define goals via explicit force vectors, and then the model generates the actions that make that force happen. We train the video model on synthetic data from physics simulators.
Dataset Details
Dataset Description
We train the model on a synthetic dataset of simple causal chains (colliding balls, falling dominos) and complex dynamics (swaying flowers), generated using Blender and PhysDreamer. This dataset contains three scenarios:
- Dominos (3k videos): Generated in Blender, these videos show a line of dominos where a direct force on one initiates a chain reaction, linking the “cause” to a “goal force” on a downstream domino.
- Rolling Balls (6k videos): Blender scenes of multiple balls. A direct force is applied to a “projectile” ball, which is aimed to either collide with a “target” ball (4.5k videos) or miss it (1.5k videos).
- PhysDreamer Carnation (3k videos): Videos of a flower swaying after being poked, generated with PhysDreamer, a method that integrates 3D Gaussians and a physics simulator. This component teaches the model complex, non-rigid dynamics from a direct force.
- Shared by: [Brown University]
- License: [MIT]
Dataset Sources
- Paper: [https://arxiv.org/abs/2601.05848v1]
- Demo: [https://goal-force.github.io/]
See our project page [https://goal-force.github.io/] for more information.