| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| pretty_name: MVVBench |
| size_categories: |
| - 10K<n<100K |
| task_categories: |
| - video-classification |
| - visual-question-answering |
| - video-text-to-text |
| tags: |
| - video |
| - annotations |
| - ego-exo4d |
| - cmu-panoptic |
| - mmptrack |
| - multi-view |
| source_datasets: |
| - extended|other-ego-exo4d |
| - extended|other-cmu-panoptic |
| - extended|other-mmptrack |
| annotations_creators: |
| - expert-generated |
| language_creators: |
| - found |
| multilinguality: |
| - monolingual |
| extra_gated_prompt: > |
| This dataset contains only annotations. Users must separately obtain |
| the source videos from Ego-Exo4D, CMU Panoptic Studio, and MMPTRACK |
| under each dataset's original license. Commercial use is restricted |
| due to source-dataset terms. |
| extra_gated_fields: |
| Name: text |
| Affiliation: text |
| I agree to use this dataset for non-commercial research only: checkbox |
| I have read and will comply with the source dataset licenses: checkbox |
| --- |
| |
| # Multi-View Video QA Dataset |
|
|
| This dataset contains 1,323 question-answer pairs across 608 multi-view video samples for video question answering research. |
|
|
| ## Repository Structure |
|
|
| ``` |
| . |
| ├── annotations/ |
| │ ├── README.md # This file |
| │ ├── all_questions.json # QA annotations (original format) |
| │ ├── all_questions.parquet # QA annotations (parquet format) |
| │ ├── video_mapping.csv # Video file mapping (source -> target) |
| │ └── prepare_videos.py # Script to prepare videos from source datasets |
| │ |
| ├── dataset/ # Source datasets (user must download) |
| │ ├── egoexo/ |
| │ │ └── takes/ |
| │ │ ├── sfu_basketball_01_10/ |
| │ │ │ └── frame_aligned_videos/ |
| │ │ │ └── downscaled/ |
| │ │ │ └── 448/ |
| │ │ │ ├── aria01_1201-1.mp4 |
| │ │ │ ├── cam01.mp4 |
| │ │ │ └── ... |
| │ │ └── ... |
| │ │ |
| │ ├── mmptrack/ |
| │ │ ├── train/ |
| │ │ │ └── videos/ |
| │ │ │ ├── 63am/ |
| │ │ │ │ └── cafe_shop_0/ |
| │ │ │ │ ├── cafe_shop_0_camera1.mp4 |
| │ │ │ │ └── ... |
| │ │ │ └── 64am/ |
| │ │ └── validation/ |
| │ │ └── videos/ |
| │ │ └── 64pm/ |
| │ │ |
| │ └── panoptic/ |
| │ ├── 160224_haggling1/ |
| │ │ └── hdVideos_down/ |
| │ │ ├── hd_00_00.mp4 |
| │ │ └── ... |
| │ └── ... |
| │ |
| └── videos/ # Prepared videos (output of prepare_videos.py) |
| ├── sfu_basketball_05_35_view0.mp4 |
| ├── sfu_basketball_05_35_view1.mp4 |
| ├── 63am_cafe_shop_0_view0.mp4 |
| ├── 160224_haggling1_view0.mp4 |
| └── ... |
| ``` |
|
|
| ## Annotation Files |
|
|
| ### `all_questions.json` / `all_questions.parquet` |
|
|
| | Column | Description | |
| |--------|-------------| |
| | `sample_name` | Video sample identifier | |
| | `views` | List of view indices used for the question (e.g., `[3, 2]`) | |
| | `question` | Question text | |
| | `choice_A`, `choice_B`, `choice_C`, `choice_D` | Multiple choice options | |
| | `correct_answer` | Correct answer key (A/B/C/D) | |
| | `gt_answer_text` | Ground truth answer text | |
| | `category` | Question category | |
| | `static_or_dynamic` | Static or dynamic scene | |
| | `timestamp_start`, `timestamp_end` | Relevant time range (seconds) | |
| | `question_type` | Type: counting, descriptive, binary | |
| | `original_negated` | Original or negated question | |
|
|
| ### `video_mapping.csv` |
| |
| Maps 3,088 video files from source datasets to standardized names: |
| |
| | Column | Description | |
| |--------|-------------| |
| | `target` | Target filename (e.g., `sfu_basketball_05_35_view0.mp4`) | |
| | `sample_name` | Sample name | |
| | `view` | View number | |
| | `dataset` | Source dataset: `egoexo`, `mmptrack`, `panoptic` | |
| | `split` | For mmptrack: `train` or `validation` | |
| | `source_rel_path` | Relative path within source dataset | |
| | `source_file` | Source filename | |
|
|
| ## Preparing Videos |
|
|
| Due to licensing restrictions, videos are not included. You must download them from the original sources and use the provided script to prepare them. |
|
|
| ### Step 1: Download Source Datasets |
|
|
| 1. **Ego-Exo4D** (2,590 files) |
| - Website: https://ego-exo4d-data.org/ |
| - Download the `takes` with `frame_aligned_videos/downscaled/448/` videos |
|
|
| 2. **MMPTrack** (272 files) |
| - Website: https://iccv2021-mmp.github.io/subpage/dataset.html |
| - Download both `train` and `validation` splits |
|
|
| 3. **CMU Panoptic** (220 files) |
| - Website: http://domedb.perception.cs.cmu.edu/ |
| - Download sequences with `hdVideos_down/` (downscaled HD videos) |
|
|
| ### Step 2: Organize Directory Structure |
|
|
| Place downloaded datasets under `dataset/` directory following the structure shown above. |
|
|
| ### Step 3: Run the Preparation Script |
|
|
| ```bash |
| # Preview first |
| python annotations/prepare_videos.py \ |
| --dataset-root ./dataset \ |
| --output-dir ./videos \ |
| --dry-run |
| |
| # Copy files |
| python annotations/prepare_videos.py \ |
| --dataset-root ./dataset \ |
| --output-dir ./videos |
| |
| # Or create symlinks (saves disk space) |
| python annotations/prepare_videos.py \ |
| --dataset-root ./dataset \ |
| --output-dir ./videos \ |
| --symlink |
| ``` |
|
|
| ## Statistics |
|
|
| | Dataset | Samples | Video Files | Categories | |
| |---------|---------|-------------|------------| |
| | Ego-Exo4D | 496 | 2,590 | Basketball, cooking, bouldering, dance, music, etc. | |
| | MMPTrack | 68 | 272 | Cafe, retail, office, lobby, industry safety | |
| | CMU Panoptic | 44 | 220 | Haggling, mafia, ultimatum, social games | |
| | **Total** | **608** | **3,082** | | |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite the original source datasets: |
|
|
| ```bibtex |
| @article{grauman2024egoexo4d, |
| title={Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives}, |
| author={Grauman, Kristen and others}, |
| journal={CVPR}, |
| year={2024} |
| } |
| |
| @inproceedings{han2021mmptrack, |
| title={MMPTrack: Large-scale Densely Annotated Multi-camera Multiple People Tracking Benchmark}, |
| author={Han, Xiaotian and others}, |
| booktitle={ICCV Workshop}, |
| year={2021} |
| } |
| |
| @inproceedings{joo2015panoptic, |
| title={Panoptic Studio: A Massively Multiview System for Social Motion Capture}, |
| author={Joo, Hanbyul and others}, |
| booktitle={ICCV}, |
| year={2015} |
| } |
| ``` |
|
|
