Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,142 @@
+---
+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.
+
+## 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)