Code for paper: Towards Tokenized Human Dynamics Representation

Video Tokneization

Codebase for video tokenization, based on our paper Towards Tokenized Human Dynamics Representation.

Prerequisites (tested under Python 3.8 and CUDA 11.1)

apt-get install ffmpeg  
pip install torch==1.8  
pip install torchvision  
pip install pytorch-lightning  
pip install pytorch-lightning-bolts  
pip install aniposelib wandb gym test-tube ffmpeg-python matplotlib easydict scikit-learn   

Data Preparation

1. Make a directory besides this repo and name it aistplusplus
2. Download from AIST++ website until it looks like

├── annotations
│   ├── cameras
│   ├── ignore_list.txt
│   ├── keypoints2d
│   ├── keypoints3d
│   ├── motions
│   └── splits
└── video_list.txt

How to run

1. Write one configuration file, e.g., configs/tan.yaml.

2. Run python pretrain.py --cfg configs/tan.yaml with GPU, which will create a folder under logs for this run. Folder name specified by the NAME in configuration file. Then run python cluster.py --cfg configs/tan.yaml (CPU-only) and check results in demo.ipynb.

3. Or you can download and unzip my training result into logs folder from here.