Code for paper: Towards Tokenized Human Dynamics Representation

Related tags

Deep Learningpytorchvideo-processinghuman-dynamics
Overview

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.

Owner
Kenneth Li
Kenneth Li
GitHub Repository
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