Skip to content

Latest commit

 

History

History
98 lines (65 loc) · 3.5 KB

README.md

File metadata and controls

98 lines (65 loc) · 3.5 KB
Raw

Capturing the Motion of Every Joint: 3D Human Pose and Shape Estimation with Independent Tokens

[project] [arxiv] [paper][examples]

The multi-person videos above are based on the VIBE detection and tracking framework.

Capturing the motion of every joint: 3D human pose and shape estimation with independent tokens,
Sen Yang, Wen Heng, Gang Liu, Guozhong Luo, Wankou Yang, Gang Yu,
The Eleventh International Conference on Learning Representations, ICLR2023 spotlight

Getting Started

This repo is based on the enviroment of python>=3.6 and PyTorch>=1.8. It's better to use the virtual enironment of conda

conda create -n int_hmr python=3.6 && conda activate int_hmr

Install PyTorch following the steps of the official guide on PyTorch website.

The models in the paper were trained using the distributed training framework Horovod. If you want to train the model distributedly using this code, please install the Horovod following the website, we use the version of horovod:0.3.3.

And install the dependencies using conda:

pip install -r requirements.txt

Data preparation

We follow the steps of MAED repo to prepare the training data. Please refer to data.md

Training

To run on a machine with 4 GPUs:

sh hvd_start.sh 4 localhost:4

To run on 4 machines with 4 GPUs each

sh hvd_start.sh 16 server1_ip:4,server2_ip:4,server3_ip:4,server4_ip:4

Here we show the training commands of using a single machine with 4 GPUs for the proposed scheme of progressive 3-stage training.

1.Image based pre-training:

sh exp/phase1/hvd_start.sh 4 localhost:4

2.Image/Video based pre-training:

sh exp/phase2/hvd_start.sh 4 localhost:4

3.Fine-tuning:

sh exp/phase3/hvd_start.sh 4 localhost:4

Evaluation

sh exp/eval/hvd_start.sh 4 localhost:4

Pretrained models

PA-MPJPE (3DPW test set) Length of temp embed. Link
42.0 (T=64) 16 Model-1 Google drive
42.3 (T=64) 64 Model-2 Google drive

Citation

If you find this repository useful please give it a star 🌟 or consider citing our work:

@inproceedings{
yang2023capturing,
title={Capturing the Motion of Every Joint: 3D Human Pose and Shape Estimation with Independent Tokens},
author={Sen Yang and Wen Heng and Gang Liu and GUOZHONG LUO and Wankou Yang and Gang YU},
booktitle={The Eleventh International Conference on Learning Representations (ICLR) },
year={2023},
url={https://openreview.net/forum?id=0Vv4H4Ch0la}
}

Credit

Thanks for the great open-source codes of MAED and VIBE