PhysInOne: Visual Physics Learning and Reasoning in One Suite
We present PhysInOne, a large-scale synthetic dataset addressing the critical scarcity of physically-grounded training data for AI systems. Unlike existing datasets limited to merely hundreds or thousands of examples, PhysInOne provides 2 million videos across 153,810 dynamic 3D scenes, covering 71 basic physical phenomena in mechanics, optics, fluid dynamics, and magnetism. Distinct from previous works, our scenes feature multiobject interactions against complex backgrounds, with comprehensive ground-truth annotations including 3D geometry, semantics, dynamic motion, physical properties, and text descriptions. We demonstrate PhysInOne's efficacy across four emerging applications: physics...
| Authors | Siyuan Zhou, Hejun Wang, Hu Cheng et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
Models and datasets referencing this paper
- vLAR/PhysInOneDataset
- PhysInOneP01/PhysInOneP01Dataset
- PhysInOneP10/PhysInOneP10Dataset
- PhysInOneP05/PhysInOneP05Dataset
- PhysInOneP15/PhysInOneP15Dataset
- PhysInOneP03/PhysInOneP03Dataset
- PhysInOneP04/PhysInOneP04Dataset
- PhysInOneP08/PhysInOneP08Dataset
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