---
language:
- en
license: mit
task_categories:
- visual-question-answering
tags:
- vision
- benchmark
- spatial-reasoning
- causal-reasoning
configs:
- config_name: collision
  data_files: "collision/*"
- config_name: compatibility
  data_files: "compatibility/*"
- config_name: occlusion
  data_files: "occlusion/*"
- config_name: physics
  data_files: "physics/*"
- config_name: realworld
  data_files: "realworld/*"
---

# CausalSpatial

**CausalSpatial** is a visual question answering benchmark for evaluating object-centric causal spatial reasoning in vision-language models. Each question presents a 3D-rendered scene and asks the model to reason about physical outcomes — not just what is visible, but what *would happen* given a specific action or trajectory.

## Dataset Structure

### Synthetic subsets (`collision`, `compatibility`, `occlusion`, `physics`)

| Field | Type | Description |
|---|---|---|
| `id` | string | Unique sample identifier |
| `question` | string | Multiple-choice question |
| `answer` | string | Correct option label (e.g. `A`, `B`) |
| `image` | image | Rendered scene image |
| `not_sure` | string | Option label corresponding to "Not sure" |

### Real-world subset (`realworld`)

| Field | Type | Description |
|---|---|---|
| `id` | string | Unique sample identifier |
| `type` | string | Question category (collision / occlusion / compatibility / trajectory) |
| `question` | string | Multiple-choice question |
| `options` | list[string] | Answer choices (e.g. `["Yes", "No"]`) |
| `answer` | string | Correct answer text |
| `image` | image | Real-world scene photo |

## Subsets

### `collision` — 826 samples
A toy car is placed on a floor with objects nearby. The model must judge whether the car will collide with something if it moves forward, and if so, which object to remove to prevent the collision.

> *Note: Floor strip spacing encodes depth perspective.*

### `compatibility` — 99 samples
An object is suspended above a container. The model must judge whether the object can fit into the container if it falls freely.

> *Note: The falling object is positioned directly above the container.*

### `occlusion` — 189 samples
A car is parked in front of a cabinet. The model must judge whether an item inside the cabinet can be retrieved without being blocked, depending on the car's direction of movement.

### `physics` — 311 samples
Ball trajectory prediction across three sports scenarios:
- **Soccer**: Will the ball enter the goal along the shown trajectory?
- **Basketball**: Will the ball go through the basket along the shown arc?
- **Billiard**: Will a ball reach a pocket given the cue ball's direction?

### `realworld` — 116 samples
Real-world driving and street scene photographs. The model must reason about physical outcomes across four question types:
- **collision**: Will a moving vehicle collide with another object?
- **occlusion**: Will an object be occluded given a movement?
- **compatibility**: Can an object fit into or pass through a space?
- **trajectory**: What trajectory will a moving object follow?

## Usage

```python
from datasets import load_dataset

ds = load_dataset("Mwxinnn/CausalSpatial", name="collision")
ds = load_dataset("Mwxinnn/CausalSpatial", name="physics")
ds = load_dataset("Mwxinnn/CausalSpatial", name="compatibility")
ds = load_dataset("Mwxinnn/CausalSpatial", name="occlusion")
ds = load_dataset("Mwxinnn/CausalSpatial", name="realworld")

print(ds["train"][0])
```

## License

MIT