AVISet

AVISet is a large-scale dataset for mask-guided, text-conditioned video editing. Each sample pairs a source video with a temporally aligned mask video that identifies the editable subject or region. Natural-language captions describe the source content, and the test split additionally provides an editing prompt describing the desired edited result.

AVISet is the official dataset released with Audio-sync Video Instance Editing with Granularity-Aware Mask Refiner.

The dataset contains 73,505 samples across training, validation, and test splits. All media is packaged into independently extractable TAR shards for reliable downloading and large-scale data loading.

AVI-Edit

• Paper: arXiv:2512.10571
• Code: github.com/suimuc/AVI-Edit-Framework
• Project page: hjzheng.net/projects/AVI-Edit

Dataset Summary

Split	Samples	Source videos	Mask videos	TAR shards	TAR size
Training	71,505	71,505	71,505	36	170.67 GB
Validation	1,000	1,000	1,000	1	2.38 GB
Test	1,000	1,000	1,000	1	2.30 GB
Total	73,505	73,505	73,505	38	175.35 GB

The training and validation splits provide source captions. The test split also contains editing_prompt, which describes the intended transformation of the masked subject or region.

Repository Structure

AVISet/
├── README.md
├── training.csv
├── validating.csv
├── testing.csv
├── training/
│   ├── part_01.tar
│   ├── part_02.tar
│   ├── ...
│   └── part_36.tar
├── validating/
│   └── part_01.tar
└── testing/
    └── part_01.tar

Each sample contains two MP4 files with matching numeric identifiers:

000000.mp4
000000_mask.mp4
000001.mp4
000001_mask.mp4
...

• <id>.mp4 is the source video.
• <id>_mask.mp4 is its temporally aligned mask video.
• White mask pixels identify the editable foreground or subject.
• Black mask pixels identify regions intended to remain unchanged.

The source and mask videos have matching frame counts, frame rates, durations, and spatial dimensions. Source videos may contain an audio track, while mask videos contain video only. Media properties such as resolution and frame rate can vary across samples.

CSV Schema

Training and validation

training.csv and validating.csv contain the following fields:

Field	Type	Description
path	string	Path to the source video relative to the extracted dataset root, for example training/000000.mp4.
mask_path	string	Path to the corresponding mask video, for example training/000000_mask.mp4.
caption	string	Natural-language description of the source video's subjects, actions, appearance, and scene.

Test

testing.csv contains one additional field:

Field	Type	Description
path	string	Path to the source video relative to the extracted dataset root, for example testing/000000.mp4.
mask_path	string	Path to the corresponding mask video, for example testing/000000_mask.mp4.
caption	string	Natural-language description of the original source video.
editing_prompt	string	Text description of the desired edited video, especially the intended transformation of the masked subject or region.

An abbreviated test record looks like:

path,mask_path,caption,editing_prompt
testing/000000.mp4,testing/000000_mask.mp4,"A young man appears to be engaged in a conversation...","A young woman with long dark hair appears to be engaged in a conversation..."

Download

Download the complete dataset with the Hugging Face CLI:

huggingface-cli download suimu/AVISet \
  --repo-type dataset \
  --local-dir AVISet

To download only selected files or splits, use --include. For example:

# Validation metadata and media only
huggingface-cli download suimu/AVISet \
  --repo-type dataset \
  --include "validating.csv" "validating/*" \
  --local-dir AVISet

# Test metadata and media only
huggingface-cli download suimu/AVISet \
  --repo-type dataset \
  --include "testing.csv" "testing/*" \
  --local-dir AVISet

Extraction

Extract each split into a directory with the same name as the CSV path prefix:

mkdir -p data/training data/validating data/testing

for shard in AVISet/training/part_*.tar; do
  tar -xf "$shard" -C data/training
done

for shard in AVISet/validating/part_*.tar; do
  tar -xf "$shard" -C data/validating
done

for shard in AVISet/testing/part_*.tar; do
  tar -xf "$shard" -C data/testing
done

cp AVISet/training.csv AVISet/validating.csv AVISet/testing.csv data/

The resulting layout is:

data/
├── training.csv
├── validating.csv
├── testing.csv
├── training/
│   ├── 000000.mp4
│   ├── 000000_mask.mp4
│   └── ...
├── validating/
│   ├── 000000.mp4
│   ├── 000000_mask.mp4
│   └── ...
└── testing/
    ├── 000000.mp4
    ├── 000000_mask.mp4
    └── ...

Each TAR shard can be extracted independently. Files in different shards use unique identifiers within their split, so all shards for a split can be extracted into the same directory.

Citation

If you find AVISet or AVI-Edit useful for your research, please cite:

@article{avi-edit,
  title={Audio-sync Video Instance Editing with Granularity-Aware Mask Refiner},
  author={Zheng, Haojie and Weng, Shuchen and Liu, Jingqi and Yang, Siqi and Shi, Boxin and Wang, Xinlong},
  journal={arXiv preprint arXiv:2512.10571},
  year={2025}
}

License

The repository declares the Apache License 2.0. Users are responsible for verifying that their intended use also complies with any rights and restrictions applicable to the underlying media.