Datasets:
license: cc-by-4.0
task_categories:
- visual-question-answering
- video-classification
language:
- en
tags:
- physics
- vision-language
- synthetic
- mujoco
- phiflow
- simulation
- physical-reasoning
size_categories:
- 10K<n<100K
modality:
- image
- video
- text
pretty_name: PhysSim-VLM Dataset
PhysSim-VLM Dataset
Paper: Synthetic Physics as Supervision: Learning Real-World Physical Reasoning in Vision-Language Models
Venue: AI4Physics Workshop @ ICML 2026
Authors: Swastik R, Natesha B V (IIIT Raichur)
Dataset Description
PhysSim-VLM is a fully synthetic physics-reasoning dataset for training and evaluating vision-language models (VLMs). It contains 15,000 multi-frame scenes (train: 12,023 / val: 1,477 / test: 1,500) generated from two physics simulators:
- MuJoCo — rigid-body dynamics: time-to-collision (TTC), pile stability, projectile trajectory
- PhiFlow — continuum fluid simulation: flow direction, viscosity comparison, fluid level
Each example consists of an 8-frame video rollout of geometric objects (coloured boxes, spheres, cylinders) interacting under physical laws, paired with a free-text question and an answer derived directly from simulator ground-truth state — no human annotation involved.
Intended Use
- Fine-tuning VLMs on physics-grounded visual reasoning
- Studying synthetic-to-real transfer for physical reasoning
- Probing what physics concepts can be taught via simulator supervision alone
Dataset Structure
| Split | Size |
|---|---|
| train | 12,023 |
| val | 1,477 |
| test | 1,500 |
Fields
| Field | Type | Description |
|---|---|---|
scene_id |
string | Unique scene identifier |
task |
string | Task family (e.g., ttc, stability, trajectory, fluid_direction, fluid_viscosity, fluid_level) |
frames_b64 |
list[string] | 1–8 video frames encoded as base64 PNG strings |
reasoning |
string | Free-text chain-of-thought answer derived from simulator state |
config |
dict | Scene configuration (object properties, simulator parameters) |
Data Generation
Scenes are generated using:
- MuJoCo 3.x for rigid-body physics (collision detection, gravity, friction)
- PhiFlow for fluid simulation (Navier-Stokes incompressible flow)
Generation scripts are available in the project code repository:
https://github.com/Swastikr/PhysSim-VLM
Citation
@inproceedings{swastik2026physsim,
title = {Synthetic Physics as Supervision: Learning Real-World Physical Reasoning in Vision-Language Models},
author = {Swastik R and Natesha B V},
booktitle = {AI4Physics Workshop at ICML 2026},
year = {2026},
url = {https://huggingface.co/datasets/Swastikr/PhysSim-VLM-Dataset}
}