| --- |
| 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](https://github.com/Swastikr/PhysSim-VLM) |
|
|
| ## Citation |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
| ## License |
|
|
| [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) |
|
|
