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metadata
dataset_info:
  features:
    - name: sample_key
      dtype: string
    - name: vid0_thumbnail
      dtype: image
    - name: vid1_thumbnail
      dtype: image
    - name: videos
      dtype: string
    - name: action
      dtype: string
    - name: action_name
      dtype: string
    - name: action_description
      dtype: string
    - name: source_dataset
      dtype: string
    - name: sample_hash
      dtype: int64
    - name: retrieval_frames
      dtype: string
    - name: differences_annotated
      dtype: string
    - name: differences_gt
      dtype: string
    - name: split
      dtype: string
  splits:
    - name: test
      num_bytes: 15523770
      num_examples: 557
  download_size: 6621934
  dataset_size: 15523770
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*

Dataset card for "VidDiffBench" benchmark

This is the dataset / benchmakark is for Video Action Differencing (ICLR 2025). Video Action Differencing is a new task that compares how an action is performed between two videos.

This page explains the dataset structure and how to download it. See the paper for details on dataset construction. The code for running evaluation, benchmarking popular LMMs, and implementing our method is at https://jmhb0.github.io/viddiff

@inproceedings{burgessvideo,
  title={Video Action Differencing},
  author={Burgess, James and Wang, Xiaohan and Zhang, Yuhui and Rau, Anita and Lozano, Alejandro and Dunlap, Lisa and Darrell, Trevor and Yeung-Levy, Serena},
  booktitle={The Thirteenth International Conference on Learning Representations}
}

The Video Action Differencing task: closed and open evaluation

The general task with a picture.

Closed mode: (discuss a bit)

Open mode:

Dataset structure

Follow the next section to access the data: we have dataset is a HuggingFace dataset and videos is a list. For row i: video A is videos[0][i], video B is videos[1][i], and dataset[i] is the annotation for the difference between the videos.

The videos:

  • videos[0][i]['video'] and is a numpy array with shape (nframes,H,W,3).
  • videos[0][i]['fps_original'] is an int, frames per second.

The annotations:

  • sample_key a unique key.
  • videos metadata about the videos A and B used by the dataloader.
  • action action key like "fitness_2"
  • action_name a short action name, like "deadlift"
  • action_description a longer action description, like "a single free weight deadlift without any weight"
  • source_dataset the source dataset for the videos (but not annotation), e.g. 'humman' here.
  • differences_annotated a dict of

Getting the data

Getting the dataset requires a few steps. We distribute the annotations, but since we don't own the videos, you'll have to download them elsewhere.

Get the annotations

First, get the annotations from the hub like this:

from datasets import load_dataset
repo_name = "viddiff/VidDiffBench"
dataset = load_dataset(repo_name)

Get the videos

We get videos from prior works (which should be cited if you use the benchmark - see the end of this doc). The source dataset is in the dataset column source_dataset.

First, download some .py files from this repo into your local data/ file.

GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/viddiff/VidDiffBench data/

A few datasets let us redistribute videos, so you can download them from this HF repo like this: 

python data/download_data.py

If you ONLY need the 'easy' split, you can stop here. The videos includes the source datasets Humann (and 'easy' only draws from this data) and JIGSAWS.

For 'medium' and 'hard' splits, you'll need to download these other datasets from the EgoExo4D and FineDiving. Here's how to do that:

Download EgoExo4d videos

These are needed for 'medium' and 'hard' splits. First Request an access key from the docs (it takes 48hrs). Then follow the instructions to install the CLI download tool egoexo. We only need a small number of these videos, so get the uids list from data/egoexo4d_uids.json and use egoexo to download:

uids=$(jq -r '.[]' data/egoexo4d_uids.json | tr '\n' ' ' | sed 's/ $//')
egoexo -o data/src_EgoExo4D --parts downscaled_takes/448 --uids $uids

Common issue: remember to put your access key into ~/.aws/credentials.

Download FineDiving videos

These are needed for 'medium' split. Follow the instructions in the repo to request access (it takes at least a day), download the whole thing, and set up a link to it:

ln -s <path_to_fitnediving> data/src_FineDiving

Making the final dataset with videos

Install these packages:

pip install numpy Pillow datasets decord lmdb tqdm huggingface_hub

Now you can load a dataset, and then load videos. The dataset splits are 'easy', 'medium', and 'hard'.

from data.load_dataset import load_dataset, load_all_videos
dataset = load_dataset(splits=['easy'], subset_mode="0")
videos = load_all_videos(dataset, cache=True, cache_dir="cache/cache_data")

Here, videos[0] and videos[1] are lists of length len(dataset). Each sample has two videos to compare, so for sample i, video A is videos[0][i] and video B is videos[0][i]. For video A, the video itself is videos[0][i]['video'] and is a numpy array with shape (nframes,3,H,W); the fps is in videos[0][i]['fps_origi'].

By passing the argument cache=True to load_all_videos, we create a cache directory at cache/cache_data/, and save copies of the videos using numpy memmap (total directory size for the whole dataset is 55Gb). Loading the videos and caching will take a few minutes per split (faster for the 'easy' split), and about 25mins for the whole dataset. But on subsequent runs, it should be fast - a few seconds for the whole dataset.

Finally, you can get just subsets, for example setting subset_mode=3_per_action will take 3 video pairs per action.

License

The annotations and all other non-video metadata is realeased under an MIT license.

The videos retain the license of the original dataset creators, and the source dataset is given in dataset column source_dataset.

Citation

Below is the citation for our paper, and the original source datasets:

@inproceedings{burgessvideo,
  title={Video Action Differencing},
  author={Burgess, James and Wang, Xiaohan and Zhang, Yuhui and Rau, Anita and Lozano, Alejandro and Dunlap, Lisa and Darrell, Trevor and Yeung-Levy, Serena},
  booktitle={The Thirteenth International Conference on Learning Representations}
}


@inproceedings{cai2022humman,
  title={{HuMMan}: Multi-modal 4d human dataset for versatile sensing and modeling},
  author={Cai, Zhongang and Ren, Daxuan and Zeng, Ailing and Lin, Zhengyu and Yu, Tao and Wang, Wenjia and Fan,
          Xiangyu and Gao, Yang and Yu, Yifan and Pan, Liang and Hong, Fangzhou and Zhang, Mingyuan and
          Loy, Chen Change and Yang, Lei and Liu, Ziwei},
  booktitle={17th European Conference on Computer Vision, Tel Aviv, Israel, October 23--27, 2022,
             Proceedings, Part VII},
  pages={557--577},
  year={2022},
  organization={Springer}
}
          
@inproceedings{parmar2022domain,
  title={Domain Knowledge-Informed Self-supervised Representations for Workout Form Assessment},
  author={Parmar, Paritosh and Gharat, Amol and Rhodin, Helge},
  booktitle={Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XXXVIII},
  pages={105--123},
  year={2022},
  organization={Springer}
}

@inproceedings{grauman2024ego,
  title={Ego-exo4d: Understanding skilled human activity from first-and third-person perspectives},
  author={Grauman, Kristen and Westbury, Andrew and Torresani, Lorenzo and Kitani, Kris and Malik, Jitendra and Afouras, Triantafyllos and Ashutosh, Kumar and Baiyya, Vijay and Bansal, Siddhant and Boote, Bikram and others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={19383--19400},
  year={2024}
}

@inproceedings{gao2014jhu,
  title={Jhu-isi gesture and skill assessment working set (jigsaws): A surgical activity dataset for human motion modeling},
  author={Gao, Yixin and Vedula, S Swaroop and Reiley, Carol E and Ahmidi, Narges and Varadarajan, Balakrishnan and Lin, Henry C and Tao, Lingling and Zappella, Luca and B{\'e}jar, Benjam{\i}n and Yuh, David D and others},
  booktitle={MICCAI workshop: M2cai},
  volume={3},
  number={2014},
  pages={3},
  year={2014}
}