hongsukchoi / TCMR_RELEASE

Official Pytorch implementation of "Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video", CVPR 2021
MIT License
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3d-human-mesh 3d-human-motion 3d-human-shape-and-pose-estimation cvpr2021 temporal video

TCMR: Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video

Qualtitative result Paper teaser video
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News

Introduction

This repository is the official Pytorch implementation of Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video. Find more qualitative results here. The base codes are largely borrowed from VIBE.

Installation

TCMR is tested on Ubuntu 20.04 with Pytorch 1.12 + CUDA 11.3 and Python 3.9. Previously, it was tested on Ubuntu 16.04 with Pytorch 1.4 and Python 3.7.10. You may need sudo privilege for the installation.

source scripts/install_pip.sh

If you have a problem related to torchgeometry, please check this out.

Quick demo

Results

Here I report the performance of TCMR.

table table

See our paper for more details.

Running TCMR

Download pre-processed data (except InstaVariety dataset) from here. Pre-processed InstaVariety is uploaded by VIBE authors here. You may also download datasets from sources and pre-process yourself. Refer to this. Put SMPL layers (pkl files) under ${ROOT}/data/base_data/.

The data directory structure should follow the below hierarchy.

${ROOT}  
|-- data  
|   |-- base_data  
|   |-- preprocessed_data  
|   |-- pretrained_models

Evaluation

Reproduction (Training)

Reference

@InProceedings{choi2020beyond,
  title={Beyond Static Features for Temporally Consistent 3D Human Pose and Shape from a Video},
  author={Choi, Hongsuk and Moon, Gyeongsik and Chang, Ju Yong and Lee, Kyoung Mu},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)}
  year={2021}
}

License

This project is licensed under the terms of the MIT license.

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