$${\color{orange}Notice}$$ The paper originally associated with this code ("Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision Transformers") has evolved into PI-ViT and PAAB. Please refer to these works instead! $${\color{orange}Notice}$$

Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision Transformers

This is the code for the paper Seeing the Pose in the Pixels: Learning Pose-Aware Representations in Vision Transformers, by Dominick Reilly, Aman Chadha, and Srijan Das. we introduce two strategies for learning pose-aware representations in ViTs. The first method, called Pose-aware Attention Block (PAAB), is a plug-and-play ViT block that performs localized attention on pose regions within videos. The second method, dubbed Pose-Aware Auxiliary Task (PAAT), presents an auxiliary pose prediction task optimized jointly with the primary ViT task.

Installation

This repository is built on top of TimeSformer and follows a similar installation.

First, create a conda virtual environment and activate it:

conda create -n poseawarevt python=3.7 -y
source activate poseawarevt

Then, install the following packages:

• torchvision: pip install torchvision or conda install torchvision -c pytorch
• fvcore: pip install 'git+https://github.com/facebookresearch/fvcore'
• simplejson: pip install simplejson
• einops: pip install einops
• timm: pip install timm
• PyAV: conda install av -c conda-forge
• psutil: pip install psutil
• scikit-learn: pip install scikit-learn
• OpenCV: pip install opencv-python
• tensorboard: pip install tensorboard

Lastly, build the codebase by running:

git clone https://github.com/dominickrei/PoseAwareVT
cd PoseAwareVT
python setup.py build develop

Data preparation

We make use of the following action recognition datasets for evaluation: Toyota Smarthome, NTU RGB+D, and Northwestern-UCLA. Download the datasets from their respective sources and structure their directories in the following formats.

Smarthome

├── Smarthome
    ├── mp4
        ├── Cook.Cleandishes_p02_r00_v02_c03.mp4
        ├── Cook.Cleandishes_p02_r00_v14_c03.mp4
        ├── ...
    ├── skeletonv12
        ├── Cook.Cleandishes_p02_r00_v02_c03_pose3d.json
        ├── Cook.Cleandishes_p02_r00_v14_c03_pose3d.json
        ├── ...

NTU RGB+D

├── NTU
    ├── rgb
        ├── S001C001P001R001A001_rgb.avi
        ├── S001C001P001R001A001_rgb.avi
        ├── ...
    ├── skeletons
        ├── S001C001P001R001A001.skeleton.npy
        ├── S001C001P001R001A001.skeleton.npy
        ├── ...

• By default, the downloaded skeletons are in MATLAB format. We convert them into Numpy format using code provided in https://github.com/shahroudy/NTURGB-D/tree/master/Python

Northwestern-UCLA

├── NUCLA
    ├── ucla_rgb
        ├── view_1
            ├── a01_s01_e00.mp4
            ├── a01_s01_e01.mp4
            ├── ...
        ├── view_2
            ├── a01_s01_e00.mp4
            ├── a01_s01_e01.mp4
            ├── ...
        ├── view_3
            ├── a01_s01_e00.mp4
            ├── a01_s01_e01.mp4
            ├── ...
    ├── ucla_skeletons
        ├── S01A01E01V01_skeleton2d.json
        ├── S01A01E01V01_skeleton2d.json
        ├── ...

• By default, Northwestern-UCLA does not provide 2D skeletons. We extract 2D skeletons using OpenPose and format them in the same way as Toyota Smarthome skeletons.
  • (coming soon) Google Drive download of extracted 2D skeletons

Preparing CSVs

After downloading and preparing the datasets, prepare the CSVs for training, testing, and validation splits as train.csv, test.csv, and val.csv. The format of each CSV is:

path_to_video_1,path_to_video_1_2dskeleton,label_1
path_to_video_2,path_to_video_2_2dskeleton,label_2
...
path_to_video_N,path_to_video_N_2dskeleton,label_N

Usage

First, download the Kinetics pre-trained backbone TimeSformer model from here. Then, update TRAIN.CHECKPOINT_FILE_PATH in each config to point to the downloaded model. We provide two configs, configs/PAAB_SH_CS.yaml and configs/PAAT_SH_CS.yaml, that can be use to train TimeSformer with PAAB and PAAT respectively. By default, the config is setup for training on the Smarthome CS protocol. Modify the configs to train on other datasets (see comments in the config files).

Train a TimeSformer model on Smarthome with a single PAAB inserted at position 12 with the following command:

python tools/run_net.py --cfg configs/PAAB_SH_CS.yaml NUM_GPUS 4 TRAIN.BATCH_SIZE 32 TEST.BATCH_SIZE 32

Train a TimeSformer model on Smarthome with PAAT inserted at position 1 with the following command:

python tools/run_net.py --cfg configs/PAAT_SH_CS.yaml NUM_GPUS 4 TRAIN.BATCH_SIZE 32 TEST.BATCH_SIZE 32

Acknowledgement

We thank Facebook Research for their TimeSformer implementation and the CMU Perceptual Computing Lab for their OpenPose implementation.