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https://github.com/jfzhang95/BMP
[CVPR 2021] Body Meshes as Points.
https://github.com/jfzhang95/BMP
Last synced: about 2 months ago
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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 (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-01-24T11:45:44.000Z (over 2 years ago)
- Last Synced: 2024-02-10T15:43:47.241Z (5 months ago)
- Language: Python
- Homepage:
- Size: 6.81 MB
- Stars: 93
- Watchers: 13
- Forks: 7
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- 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:**
## 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:**
## 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.