---
license: apache-2.0
pretty_name: DynSuperCLEVR
language:
- en
task_categories:
- visual-question-answering
- video-text-to-text
tags:
- video-question-answering
- physics-reasoning
- 4d-dynamic-scenes
- compositional-reasoning
- spatial-reasoning
- synthetic
- clevr
- superclevr
size_categories:
- 10K<n<100K
configs:
- config_name: questions
  data_files:
  - split: train
    path: viewer/train.parquet
  - split: val
    path: viewer/val.parquet
---

# DynSuperCLEVR

**Compositional 4D Dynamic Scenes Understanding with Physics Priors for Video Question Answering**
*ICLR 2025*

DynSuperCLEVR is a video question answering (VQA) benchmark for understanding **4D (3D + temporal) object dynamics** and **physical properties** in dynamic scenes. Unlike most video QA datasets that test appearance and 2D motion, DynSuperCLEVR targets the physical concepts that require an explicit 4D scene understanding: **velocity, acceleration, and collisions**. It asks models not only to describe what happened, but to **predict** what will happen next and to reason about **counterfactual** "what-if" scenarios under altered physical conditions.

| | |
|---|---|
| 🌐 **Project page** | https://xingruiwang.github.io/projects/DynSuperCLEVR/ |
| 📄 **Paper (arXiv)** | https://arxiv.org/abs/2406.00622 |
| 📄 **Paper (OpenReview)** | https://openreview.net/pdf?id=6Vx28LSR7f |
| 🧠 **Model code (NS-4DPhysics)** | https://github.com/XingruiWang/NS-4DPhysics |
| 🛠️ **Dataset generation code** | https://github.com/XingruiWang/DynSuperCLEVR |
| 📊 **Poster** | https://iclr.cc/virtual/2025/poster/30879 |

---

## Highlights

- **Physics-grounded VQA.** Questions are built around three physical concepts — **velocity**, **acceleration**, and **collision** — that cannot be answered from a single frame and require reasoning over object dynamics through time.
- **Three reasoning types.** **Factual** (what is happening), **Predictive** (what will happen next), and **Counterfactual** (what would happen under a different initial condition, e.g. "if the engine were turned on" / "if it were static").
- **Full 4D annotations.** Every scene ships with complete physical ground truth: per-frame 3D positions, linear & angular velocities, camera parameters (intrinsics `K` and extrinsics `R`), instance segmentation, and collision events (contact normal, force, frame, image position).
- **Rendered with a physics engine.** Scenes are generated by extending the [Kubric](https://github.com/google-research/kubric) pipeline (built on PyBullet) over the [SuperCLEVR](https://github.com/Lizw14/Super-CLEVR) asset set, with added support for **acceleration** dynamics.

---

## Dataset statistics

- **1,100 videos** (rigid-body simulations), 120 rendered frames each (RGB, instance masks, segmentation).
- **12,752 questions** across three reasoning types and two splits.

| Reasoning type   | Train  | Val  | Total  |
|------------------|-------:|-----:|-------:|
| Factual          | 7,850  | 791  | 8,641  |
| Predictive       | 2,750  | 274  | 3,024  |
| Counterfactual   | 989    | 98   | 1,087  |
| **Total**        | **11,589** | **1,163** | **12,752** |

Answers are a mix of binary (`True`/`False`), direction (`left`, `right`, `front`, `back`, `up`, `down`), and attribute (color / object) labels, following the CLEVR-style compositional program format.

---

## Repository structure

```
DynSuperCLEVR/
├── questions/
│   ├── train/
│   │   ├── questions_physics_factual.json
│   │   ├── questions_physics_predictive.json
│   │   └── questions_physics_counterfactual.json
│   └── val/
│       ├── questions_physics_factual.json
│       ├── questions_physics_predictive.json
│       └── questions_physics_counterfactual.json
├── frames_upload_chunks/
│   └── frames_chunk_000.tar ... frames_chunk_023.tar   # all video frames (~325 GB), tarred
└── videos/frames/<scene>/                              # sample uncompressed scenes
    ├── rgba_00000.png ... rgba_00119.png               # RGB frames
    ├── segmentation_00000.png ...                      # semantic segmentation
    ├── mask_00000.png ...                              # instance masks
    ├── metadata.json                                   # camera + per-instance 4D state
    └── events.json                                     # collision events
```

### Question schema

Each question file is a dict with a `questions` list; every entry contains:

| Field | Description |
|---|---|
| `question` | Natural-language question |
| `answer` | Ground-truth answer |
| `program` | Compositional functional program (CLEVR-style) used to derive the answer |
| `image_filename` / `image` | The scene this question refers to (e.g. `super_clevr_0`) |
| `image_index`, `question_index`, `question_family_index` | Indices |
| `template_filename` | Question template used to generate the question |
| `frame_length` | Number of frames the question is grounded over |
| `split` | `train` / `val` |

> The **Dataset Viewer** (and the `questions` config loadable via `datasets`) is served from flattened Parquet files under `viewer/` (`train.parquet`, `val.parquet`), which merge the three reasoning types and add a `reasoning_type` column. There, the `program` field is stored as a JSON string for a stable schema; the canonical nested JSON lives under `questions/`.

### Per-scene physical ground truth (`metadata.json`)

- `camera` — intrinsics `K`, extrinsics `R`, projection of 3D → image.
- `instances` — for each object: per-frame positions, `velocities`, `angular_velocities`, plus shape / color / material attributes.
- `metadata` — scene-level config: `gravity`, `frame_rate`, `num_frames`, `num_instances`, background HDRI, resolution.
- `args` — full generation arguments (floor friction/restitution, camera mode, etc.).

### Collision events (`events.json`)

- `collisions` — list of contacts with `contact_normal`, `force`, `frame` (sub-frame precise), and `image_position`.

---

## Usage

The frame data is distributed as tar chunks. Download and extract:

```bash
# Download the whole dataset
huggingface-cli download RyanWW/DynSuperCLEVR --repo-type dataset --local-dir ./DynSuperCLEVR

# Or just the questions (small) to get started
huggingface-cli download RyanWW/DynSuperCLEVR --repo-type dataset \
    --include "questions/**" --local-dir ./DynSuperCLEVR

# Reassemble the frames
cd DynSuperCLEVR
for f in frames_upload_chunks/frames_chunk_*.tar; do tar -xf "$f"; done
```

```python
import json

qs = json.load(open("DynSuperCLEVR/questions/val/questions_physics_factual.json"))["questions"]
print(len(qs), "questions")
print(qs[0]["question"], "->", qs[0]["answer"])
# e.g. "Which direction is the red cruiser moving towards at the beginning?" -> "back"
```

---

## Example questions

| Type | Question | Answer |
|---|---|---|
| Factual | *Is the red mountain moving fast at the beginning?* | False |
| Factual | *Which direction is the red cruiser moving towards at the beginning?* | back |
| Predictive | *Will the purple minivan collide with the suv next?* | True |
| Counterfactual | *Will the green fighter collide with the mountain if it turns the engine on?* | True |

---

## Method: NS-4DPhysics

The accompanying model, **NS-4DPhysics**, is a neural-symbolic approach that builds an explicit 4D scene representation and integrates physics priors:

1. **3D scene parser** — a neural mesh model that recovers object pose and state by render-and-compare.
2. **Question parser** — translates language into a symbolic program.
3. **Program executor** — runs the program over the 4D representation, using a PyBullet physics engine to roll out dynamics for predictive / counterfactual reasoning.

On DynSuperCLEVR, NS-4DPhysics reaches **82.64%** average accuracy, substantially outperforming PO3D-VQA (62.93%), PLLaVA (59.24%), and GPT-4o with reasoning (56.06%), highlighting the gap between current video-language models and explicit physical scene understanding.

---

## Citation

```bibtex
@inproceedings{wang2025compositional,
  title     = {Compositional 4D Dynamic Scenes Understanding with Physics Priors for Video Question Answering},
  author    = {Wang, Xingrui and Ma, Wufei and Wang, Angtian and Chen, Shuo and Kortylewski, Adam and Yuille, Alan},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2025},
  eprint    = {2406.00622},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url       = {https://arxiv.org/abs/2406.00622}
}
```

## License

Released under the **Apache 2.0** license. DynSuperCLEVR builds on [SuperCLEVR](https://github.com/Lizw14/Super-CLEVR) and the [Kubric](https://github.com/google-research/kubric) generation pipeline; please also respect their respective licenses.

## Authors

Xingrui Wang, Wufei Ma, Angtian Wang (Johns Hopkins University); Shuo Chen (Tsinghua University); Adam Kortylewski (Max Planck Institute for Informatics / University of Freiburg); Alan Yuille (Johns Hopkins University).