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InfoGCN

Official PyTorch implementation of "InfoGCN: Representation Learning for Human Skeleton-based Action Recognition", CVPR22.

Abstract

<img src="resources/main_fig.png" width="600" /> Human skeleton-based action recognition offers a valuable means to understand the intricacies of human behavior because it can handle the complex relationships between physical constraints and intention. Although several studies have focused on encoding a skeleton, less attention has been paid to embed this information into the latent representations of human action. InfoGCN proposes a learning framework for action recognition combining a novel learning objective and an encoding method. First, we design an information bottleneck-based learning objective to guide the model to learn informative but compact latent representations. To provide discriminative information for classifying action, we introduce attention-based graph convolution that captures the context-dependent intrinsic topology of human action. In addition, we present a multi-modal representation of the skeleton using the relative position of joints, designed to provide complementary spatial information for joints. InfoGCN surpasses the known state-of-the-art on multiple skeleton-based action recognition benchmarks with the accuracy of 93.0% on NTU RGB+D 60 cross-subject split, 89.8% on NTU RGB+D 120 cross-subject split, and 97.0% on NW-UCLA.

Dependencies

Python >= 3.6
PyTorch >= 1.7.0
NVIDIA Apex
tqdm, tensorboardX, wandb

Data Preparation

Download datasets.

There are 3 datasets to download:

NTU RGB+D 60 Skeleton
NTU RGB+D 120 Skeleton
NW-UCLA

NTU RGB+D 60 and 120

Request dataset here: https://rose1.ntu.edu.sg/dataset/actionRecognition
Download the skeleton-only datasets:
1. nturgbd_skeletons_s001_to_s017.zip (NTU RGB+D 60)
2. nturgbd_skeletons_s018_to_s032.zip (NTU RGB+D 120)
3. Extract above files to ./data/nturgbd_raw

NW-UCLA

Download dataset from CTR-GCN repo: https://github.com/Uason-Chen/CTR-GCN
Move all_sqe to ./data/NW-UCLA

Data Processing

Directory Structure

Put downloaded data into the following directory structure:

- data/
  - NW-UCLA/
    - all_sqe
      ... # raw data of NW-UCLA
  - ntu/
  - ntu120/
  - nturgbd_raw/
    - nturgb+d_skeletons/     # from `nturgbd_skeletons_s001_to_s017.zip`
      ...
    - nturgb+d_skeletons120/  # from `nturgbd_skeletons_s018_to_s032.zip`
      ...

Generating Data

Generate NTU RGB+D 60 or NTU RGB+D 120 dataset:

 cd ./data/ntu # or cd ./data/ntu120
 # Get skeleton of each performer
 python get_raw_skes_data.py
 # Remove the bad skeleton 
 python get_raw_denoised_data.py
 # Transform the skeleton to the center of the first frame and vertically align to the ground
 python seq_transformation.py

Training & Testing

Training

We set the seed number for Numpy and PyTorch as 1 for reproducibility.
If you want to reproduce our works, please find the details in the supplementary matrials. The hyperparameter setting differs depending on the training dataset.
This is an exmaple command for training InfoGCN on NTU RGB+D 60 Cross Subject split. Please change the arguments if you want to customize the training. --k indicates k value of k-th mode represenation of skeleton. If you set --use_vel=True, the model will be trained with motion.

python main.py --half=True --batch_size=128 --test_batch_size=128 \
    --step 90 100 --num_epoch=110 --n_heads=3 --num_worker=4 --k=1 \
    --dataset=ntu --num_class=60 --lambda_1=1e-4 --lambda_2=1e-1 --z_prior_gain=3 \
    --use_vel=False --datacase=NTU60_CS --weight_decay=0.0005 \
    --num_person=2 --num_point=25 --graph=graph.ntu_rgb_d.Graph --feeder=feeders.feeder_ntu.Feeder

Testing

To test the trained models saved in <work_dir>, run the following command:

python main.py --half=True --test_batch_size=128 --n_heads=3 --num_worker=4 \
    --k=1 --dataset=ntu --num_class=60 --use_vel=False --datacase=NTU60_CS \
    --num_person=2 --num_point=25 --graph=graph.ntu_rgb_d.Graph --feeder=feeders.feeder_ntu.Feeder \
    --phase=test --save_score=True --weights=<path_to_weight>

To ensemble the results of different modalities, run the following command:

python ensemble.py \
   --dataset=ntu/xsub \
   --position_ckpts \
      <work_dir_1>/files/best_score.pkl \
      <work_dir_2>/files/best_score.pkl \
      ...
   --motion_ckpts \
      <work_dir_3>/files/best_score.pkl \
      <work_dir_4>/files/best_score.pkl \
      ...

Acknowledgements

This repo is based on 2s-AGCN and CTR-GCN. The data processing is borrowed from SGN, HCN, and Predict & Cluster.

Thanks to the original authors for their work!