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 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

ScienceQA (M-2) — Science Education

Answer: A	Answer: A

MathVision (M-3) — Competition Math

Answer: A	Answer: D

PhyX (M-4) — Physics Reasoning

Answer: C	Answer: C

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 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