README.md

---
license: other
license_name: derivative-mixed
license_link: LICENSE
task_categories:
  - visual-question-answering
  - video-classification
tags:
  - video
  - mcqa
  - vqa
  - video-generation
  - wan2.2
  - i2v
  - vbvr
size_categories:
  - 1K<n<10K
---

# Video-MCP

**Video-MCP** is a synthetic video dataset for training and evaluating video generation models on **multiple-choice question-answering (MCQA)** tasks. Each sample is a short video clip (~5 seconds) where a visual question-answering prompt is embedded directly into the video frames, and the correct answer is revealed by progressively highlighting one of four answer boxes (A/B/C/D) over the duration of the clip.

The dataset is designed for fine-tuning image-to-video models (specifically **Wan2.2-I2V-A14B**) to produce videos that "answer" visual questions by highlighting the correct option.

Output follows the **[VBVR DataFactory](https://github.com/video-reason/VBVR-DataFactory)** directory convention.

## Examples

Each clip starts with no answer highlighted, then progressively reveals the correct choice over ~5 seconds:

### CoreCognition (M-1) — General Visual Reasoning

| Answer: B | Answer: B |
|---|---|
| ![corecognition 0](examples/corecognition_0.gif) | ![corecognition 1](examples/corecognition_1.gif) |

### ScienceQA (M-2) — Science Education

| Answer: A | Answer: A |
|---|---|
| ![scienceqa 0](examples/scienceqa_0.gif) | ![scienceqa 1](examples/scienceqa_1.gif) |

### MathVision (M-3) — Competition Math

| Answer: A | Answer: D |
|---|---|
| ![mathvision 0](examples/mathvision_0.gif) | ![mathvision 1](examples/mathvision_1.gif) |

### PhyX (M-4) — Physics Reasoning

| Answer: C | Answer: C |
|---|---|
| ![phyx 0](examples/phyx_0.gif) | ![phyx 1](examples/phyx_1.gif) |

## Dataset Details

| Property | Value |
|---|---|
| **Version** | 1.0 |
| **Total samples** | 6,912 |
| **Video resolution** | 832x480 |
| **Frame count** | 81 frames per clip |
| **Frame rate** | 16 FPS |
| **Duration** | ~5.06 seconds per clip |
| **Codec** | H.264, yuv420p, MP4 container |
| **Highlight style** | darken (default) |

## Source Datasets

Video-MCP draws from four publicly available MCQA-VQA datasets on Hugging Face:

| Generator ID | Name | Source | Samples | Domain |
|---|---|---|---|---|
| M-1 | corecognition | `williamium/CoreCognition` | 753 | General visual reasoning |
| M-2 | scienceqa | `derek-thomas/ScienceQA` | 3,905 | Science education (image-only subset) |
| M-3 | mathvision | `MathLLMs/MathVision` | 1,254 | Competition math with diagrams |
| M-4 | phyx | `Cloudriver/PhyX` | 1,000 | Physics reasoning |

All source datasets are filtered to include only samples that have an associated image and exactly four answer choices (A/B/C/D).

## Data Structure

Each sample follows the [VBVR DataFactory](https://github.com/video-reason/VBVR-DataFactory) directory convention:

```
{generator_id}_{name}_data-generator/
  clip_config.json
  {name}_task/
    {name}_{NNNN}/
      first_frame.png        # Frame 0: question visible, no highlight
      prompt.txt             # Plain-text question, choices, and answer
      final_frame.png        # Last frame: correct answer fully highlighted
      ground_truth.mp4       # Full clip with progressive answer reveal
      original/
        question.json        # Structured metadata (JSON)
        <source_image>       # Original image from source dataset
```

### File Descriptions

| File | Description |
|---|---|
| `first_frame.png` | The opening frame showing the question panel (image + question text + four choices) with A/B/C/D answer boxes in the corners. No answer is highlighted. |
| `final_frame.png` | The closing frame with the correct answer box fully highlighted. |
| `ground_truth.mp4` | The complete video clip. The correct answer gradually highlights from frame 1 to the final frame (linear fade-in). |
| `prompt.txt` | Human-readable text: question, choices (A/B/C/D), and the correct answer letter. |
| `original/question.json` | Structured JSON with fields: `dataset`, `source_id`, `question`, `choices`, `answer`, `original_image_filename`. |
| `original/<image>` | The raw source image preserved with its original filename. |
| `clip_config.json` | Generator-level config: `fps`, `seconds`, `num_frames`, `width`, `height`. |

### Frame Layout

Each frame uses a two-column layout:
- **Left column**: the source VQA image, scaled to fill.
- **Right column**: question text and the four answer options.
- **Corners**: A (top-left), B (top-right), C (bottom-left), D (bottom-right) answer boxes.

### prompt.txt Format

```
What color is the object in the image?

A: Red
B: Blue
C: Green
D: Yellow

Answer: A
```

## Video Specifications

These defaults align with **Wan2.2-I2V-A14B** fine-tuning constraints:

- **Resolution**: 832x480 (width and height divisible by 8 for VAE spatial compression)
- **Frames**: 81 (satisfies `1 + 4k` for VAE temporal grid)
- **FPS**: 16
- **Duration**: ~5.06 seconds
- **Codec**: H.264, yuv420p pixel format

## Intended Use

- Fine-tuning image-to-video generation models to produce MCQA-answering videos
- Evaluating video generation models on structured visual reasoning tasks
- Research on embedding structured UI interactions into generated video

## Limitations

- All source questions are filtered to exactly 4 choices (A/B/C/D); questions with fewer or more options are excluded.
- The answer highlight is a simple linear fade-in; no complex visual dynamics.
- Source images and questions inherit any biases or errors from the upstream HF datasets.
- The dataset uses a single fixed resolution (832x480) and frame count (81).

## Citation

If you use this dataset, please cite the source datasets:

- **CoreCognition**: `williamium/CoreCognition` on Hugging Face
- **ScienceQA**: Lu et al., "Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering" (NeurIPS 2022)
- **MathVision**: Wang et al., "MathVision: Measuring Multimodal Mathematical Reasoning with Benchmarks" (2024)
- **PhyX**: `Cloudriver/PhyX` on Hugging Face

## License

This dataset is a derivative work. Each source dataset has its own license terms. Users should verify compliance with upstream licenses before redistribution.

## Generation Code

[https://github.com/video-reason/video-mcp](https://github.com/video-reason/video-mcp)