https://github.com/jfzhang95/BMP

[CVPR 2021] Body Meshes as Points.
https://github.com/jfzhang95/BMP

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[CVPR 2021] Body Meshes as Points.

• Host: GitHub
• URL: https://github.com/jfzhang95/BMP
• Owner: jfzhang95
• License: mit
• Created: 2021-03-03T12:13:06.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-01-24T11:45:44.000Z (almost 3 years ago)
• Last Synced: 2024-08-03T17:12:31.991Z (5 months ago)
• Language: Python
• Homepage:
• Size: 6.81 MB
• Stars: 95
• Watchers: 13
• Forks: 7
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-3D-human - BMP\

README

        
# Body Meshes as Points  

Code repository for the paper:  

**Body Meshes as Points**  

[Jianfeng Zhang](https://jeff95.me/), [Dongdong Yu](https://miracle-fmh.github.io/), [Jun Hao Liew](https://scholar.google.com.sg/citations?user=8gm-CYYAAAAJ&hl=en/), [Xuecheng Nie](https://niexc.github.io/), [Jiashi Feng](https://sites.google.com/site/jshfeng/)  

CVPR 2021  

[[paper](https://arxiv.org/pdf/2105.02467.pdf)] [[project page (coming soon)](https://jeff95.me/)]  

## Introduction

**Overview of the proposed method:**  

![alt text](assets/teaser.png)

## Installation instructions

This codebase was adopted from early version of mmdetection, mmcv and multiperson. Users of this repo are highly recommended to

read the readme of [mmcv](./mmcv/README.rst) and [mmdetection](./mmdetection/README.md)  and [multiperson](https://github.com/JiangWenPL/multiperson/blob/master/README.md) before using this code.

To install relevant envs:

```bash

conda env create -f environment.yml

cd mmcv

python3 setup.py install

cd ../mmdetection

./compile.sh

python setup.py develop

cd ../neural_renderer

python3 setup.py install

cd ../sdf

python3 setup.py install

```

## Prepare datasets

Please follow [multiperson](https://github.com/JiangWenPL/multiperson/blob/master/DATASETS.md) for the preparation of the dataset files.

## Run training code

Please make sure you have prepared all datasets before running our training script.

The training of our model would take two phases, pretrain -> fine tuning. We prepared two configuration files under `mmdetection/configs/bmp/`.

To train our model from scratch:

```bash

cd mmdetection

# Phase 1: Pretraining

python3 tools/train.py configs/bmp/pretrain.py --create_dummy

while true:

do

    python3 tools/train.py configs/bmp/pretrain.py

done

# We could move to next phase after training for 200 epoches

# Phase 2: Fine-tuning

python3 tools/train.py configs/bmp/finetune.py --load_pretrain ./work_dirs/pretrain/latest.pth

while true:

do

    python3 tools/train.py configs/bmp/finetune.py 

done

# It could be done after 50 epoches of training

```

## Run evaluation code

You could use the trained model to evaluate on Panoptic, MuPoTS-3D, Human3.6M and 3DPW.

Example usage:

```

cd mmdetection

bash scripts/eval_all.sh

```

## Run demo code

You could use the trained model for image inference by running:

```

cd mmdetection

bash scripts/demo.sh

```

**Qualitative results:**  

![alt text](assets/vis_bmp.png)

## Citation

If you  find this code useful for your research, please consider citing the following paper:

    @inproceedings{zhang2021bmp,

      title       = {Body Meshes as Points},

      author      = {Zhang, Jianfeng and Yu, Dongdong and Liew, Jun Hao and Nie, Xuecheng and Feng, Jiashi},

      booktitle   = {CVPR},

      year        = {2021}

    }

## Acknowledgements

This code uses ([mmcv](https://github.com/open-mmlab/mmcv), [mmdetection](https://github.com/open-mmlab/mmdetection)) as backbone, and borrows several code from [multiperson](https://github.com/JiangWenPL/multiperson), [SPIN](https://github.com/nkolot/SPIN) and [VIBE](https://github.com/mkocabas/VIBE). We gratefully appreciate the impact these libraries had on our work. If you use our code, please consider citing the original papers as well.