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Code for Video Pose Distillation

See our project website for the paper and details. Published in ICCV 2021.

@inproceedings{vpd_iccv21,
    author={Hong, James and Fisher, Matthew and Gharbi, Micha\"{e}l and Fatahalian, Kayvon},
    title={{V}ideo {P}ose {D}istillation for {F}ew-{S}hot, {F}ine-{G}rained {S}ports {A}ction {R}ecognition},
    booktitle={ICCV},
    year={2021}
}

For code in this repository, see LICENSE.

Usage

This repository contains code for VPD and VIPE*, as described in our paper.

VIPE*

To apply the VIPE* model:

./apply_vipe_model.py <pose_dir> <model_dir> -o <out_dir>
  • pose_dir : the directory containing the 2D poses for each video
  • model_dir : path to trained model
  • out_dir : path to save features to

To train a VIPE* model see train_vipe_model.py. Example: ./train_vipe_model.py --dataset 3d --save_dir <model_dir> Preprocessed 3D pose data for training is available here: VIPE-data.zip. This archive includes ground truth 3D pose and 2D pose from different camera views. Extract to data/vipe or update the paths in vipe_dataset_paths.py. For details on preprocessing, see preprocess_3d_pose.py.

A pre-trained VIPE model is available: VIPE-model.zip.

VPD

Data preparation

To prepare the sports datasets, there are several steps:

  1. Fetching the videos
  2. Pose detection / tracking
  3. Extracting crops (see extract_square_crops.py)
  4. Computing optical flow (see raft/README.md)

Our pose and tracking annotations can be found here: URL

For the source videos:

It is recommended to unzip the files to the paths defined in video_dataset_paths.py or to update those paths to where the pose files are stored. For example:

diving48
|---pose
|---crops
\---videos
fs
|---pose
|---crops
\---videos
...

To train a student model:

./train_vpd_model.py <dataset> --save_dir <model_dir> --emb_dir <teacher_dir> --flow_img <flow_name> --motion
  • dataset : the sports dataset to specialize to (e.g., fs)
  • model_dir : path to save models to
  • flow_name : the name of the flow images for the crops, which have names <frame_no>.<flow_name>.png
  • teacher_dir : path to the teacher's features

To apply a student model:

./apply_vpd_model.py <model_dir> -d <dataset> -o <out_dir> --flow_img <flow_name>
  • model_dir : path to the trained model
  • out_dir : path to save features to
  • flow_name : should be the same used for training

The student maintains the same output file formats as the teacher.

Downstream tasks:

For action recognition:

./recognize.py -d <dataset> <feature_dir>
  • dataset : the sports dataset
  • feature_dir : the directory containing the pose features

See options such as --retrieve for the retrieval task. For detection, see detect.py.

Pre-trained VPD and VIPE* features/embeddings are available at URL.

To use the Diving48 and FineGym (Floor Exercise) datasets, you need to download the labels per the READMEs in the diving48/data and finegym/data subdirectories.

Data formats

Video naming conventions

For Diving48 and FineGym, we maintain the original authors' video naming scheme.

For figure skating, videos (routines) are named by <video>_<number>_<start_frame>_<end_frame>.mp4.

For tennis, videos (points) are named by: <video>_<start_frame>_<end_frame>.mp4. Pose for each video is prefixed by front__ or back__ to denote the player.

2D pose format

Pose for each video is organized as follows:

men_olympic_short_program_2010_01_00011475_00015700
|---boxes.json
|---coco_keypoints.json.gz
|---mask.json.gz
\---meta.json

The format for boxes.json is:

[
    [frame_num, [x, y, w, h]], ...
]

The format coco_keypoints.json.gz is:

[
    [
        frame_num, [[score, [x, y, w, h], [[x, y, score] * 17]]], ...]
    ],
    ...
]

The format of mask.json.gz:

[
    [
        frame_num, [[score, [x, y, w, h], base64_encoded_png], ...]
    ],
    ...
]

Crop directories

Crops around the athlete, for training VPD, are extracted per video (see extract_square_crops.py):

men_olympic_short_program_2010_01_00011475_00015700
|---0.png           // <frame_num>.png
|---0.prev.png
|---0.flow.png
|---0.mask.png
|---1.png
|---1.prev.png
...

For tennis, the format is slightly different:

usopen_2015_mens_final_federer_djokovic
|---back
|   |---0.png       // <frame_num>.png
|   |---0.prev.png
|   |---0.flow.png
|   |---0.mask.png
|   ...
|
\---front
    |---0.png
    |---0.prev.png
    |---0.flow.png
    |---0.mask.png
    ...

Features / embedding format

Embeddings are stored as pickle files, one per video. The format for each video is:

[
    (frame_num, ndarray, {metadata dict}), ...
]

The ndarray may be 1D or 2D, depending on data augmentation (e.g., flip).