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https://github.com/jby1993/SelfReconCode
This repository contains a pytorch implementation of "SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Video (CVPR 2022, Oral)".
https://github.com/jby1993/SelfReconCode
Last synced: 3 months ago
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This repository contains a pytorch implementation of "SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Video (CVPR 2022, Oral)".
- Host: GitHub
- URL: https://github.com/jby1993/SelfReconCode
- Owner: jby1993
- Created: 2022-03-27T16:27:21.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-10-17T15:06:03.000Z (about 2 years ago)
- Last Synced: 2024-06-24T05:54:08.232Z (5 months ago)
- Language: Python
- Homepage:
- Size: 4.51 MB
- Stars: 401
- Watchers: 22
- Forks: 44
- Open Issues: 23
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- StarryDivineSky - jby1993/SelfReconCode
README
# SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Video
### | [Project Page](https://jby1993.github.io/SelfRecon/) | [Paper](https://arxiv.org/abs/2201.12792) |
This repository contains a pytorch implementation of "[SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Video (CVPR 2022, Oral)](https://arxiv.org/abs/2201.12792)".
Authors: Boyi Jiang, [Yang Hong](https://crishy1995.github.io/), [Hujun Bao](http://www.cad.zju.edu.cn/home/bao/), [Juyong Zhang](http://staff.ustc.edu.cn/~juyong/).
This code is protected under patent, and it can be only used for research purposes. For commercial uses, please send email to .
## Requirements
- Python 3
- Pytorch3d (0.4.0, some compatibility issues may occur in higher versions of pytorch3d)
Note: A GTX 3090 is recommended to run SelfRecon, make sure enough GPU memory if using other cards.
## Install
```bash
conda env create -f environment.yml
conda activate SelfRecon
bash install.sh
```
It is recommended to install pytorch3d 0.4.0 from source.
```bash
wget -O pytorch3d-0.4.0.zip https://github.com/facebookresearch/pytorch3d/archive/refs/tags/v0.4.0.zip
unzip pytorch3d-0.4.0.zip
cd pytorch3d-0.4.0 && python setup.py install && cd ..
```
To download the [SMPL](https://smpl.is.tue.mpg.de/) models from [here](https://mailustceducn-my.sharepoint.com/:f:/g/personal/jby1993_mail_ustc_edu_cn/EqosuuD2slZCuZeVI2h4RiABguiaB4HkUBusnn_0qEhWjQ?e=c6r4KS) and move pkls to smpl_pytorch/model.
## Run on PeopleSnapshot Dataset
The preprocessing of PeopleSnapshot is described here. If you want to optimize your own data, you can run [VideoAvatar](https://graphics.tu-bs.de/people-snapshot) to get the initial SMPL estimation, then follow the preprocess. Or, you can use your own SMPL initialization and normal prediction method then use SelfRecon to reconstruct.
### Preprocess
Download the [Dataset](https://graphics.tu-bs.de/people-snapshot) and unzip it to some ROOT. Run the following code to extract data for female-3-casual, for example.
```bash
python people_snapshot_process.py --root $ROOT/people_snapshot_public/female-3-casual --save_root $ROOT/female-3-casual
```
### Extract Normals
To enable our normal optimization, you have to install [PIFuHD](https://shunsukesaito.github.io/PIFuHD/) and [Lightweight Openpose](https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch) in your $ROOT1 and $ROOT2 first. Then copy generate_normals.py and generate_boxs.py to $ROOT1 and $ROOT2 seperately, and run the following code to extract normals before running SelfRecon:
```bash
cd $ROOT2
python generate_boxs.py --data $ROOT/female-3-casual/imgs
cd $ROOT1
python generate_normals.py --imgpath $ROOT/female-3-casual/imgs
```
Then, run SelfRecon with the following code, this may take one day to finish:
```bash
CUDA_VISIBLE_DEVICES=0 python train.py --gpu-ids 0 --conf config.conf --data $ROOT/female-3-casual --save-folder result
```
The results locate in $ROOT/female-3-casual/result
## Inference
Run the following code to generate rendered meshes and images.
```bash
CUDA_VISIBLE_DEVICES=0 python infer.py --gpu-ids 0 --rec-root $ROOT/female-3-casual/result/ --C
```
## Texture
This repo provides a script to utilize [VideoAvatar](https://graphics.tu-bs.de/people-snapshot) to extract the texture for the reconstructions of SelfRecon.
First, You need to install VideoAvatar and copy texture_mesh_extract.py to its repository path.
Then, after performing inference for $ROOT/female-3-casual/result, you need to simplify and parameterize the template mesh tmp.ply yourself, then save the result mesh as $ROOT/female-3-casual/result/template/uvmap.obj. And run the following code to generate the data for texture extraction:
``` bash
CUDA_VISIBLE_DEVICES=0 python texture_mesh_prepare.py --gpu-ids 0 --num 120 --rec-root $ROOT/female-3-casual/result/
```
Finally, go to VideoAvatar path, and run the following code to extract texture:
```bash
CUDA_VISIBLE_DEVICES=0 python texture_mesh_extract.py --tmp-root $ROOT/female-3-casual/result/template
```
## Dataset
The processed dataset, our trained models, some reconstruction results and textured meshes can be downloaded via the [link](https://mailustceducn-my.sharepoint.com/:f:/g/personal/jby1993_mail_ustc_edu_cn/EsSsDtUBYJVLvY21Wk2K_gQBuOWgCKFGGxr2xqheS-0ORw?e=Rda2HX), you can download and unzip some smartphone data, like CHH_female.zip, in $ROOT, and train directly with:
```bash
CUDA_VISIBLE_DEVICES=0 python train.py --gpu-ids 0 --conf config.conf --data $ROOT/CHH_female --save-folder result
```
And you can unzip CHH_female_model.zip in $ROOT/CHH_female and run:
```bash
CUDA_VISIBLE_DEVICES=0 python infer.py --gpu-ids 0 --rec-root $ROOT/CHH_female/trained/ --C
```
to check the results of our trained model in $ROOT/CHH_female/trained.
Note: If you want to train the synthetic data, config_loose.conf is prefered.
## Acknowledgement
Here are some great resources we benefit or utilize from:
- [MarchingCubes_CppCuda](https://github.com/WanquanF/MarchingCubes_CppCuda) supplies the GPU MC module (MCGpu)
- [MonoPort](https://github.com/Project-Splinter/MonoPort) and [ImplicitSegCUDA](https://github.com/Project-Splinter/ImplicitSegCUDA/tree/master/implicit_seg/cuda) constructed our Marching Cubes accelerating (MCAcc)
- [VideoAvatar](https://graphics.tu-bs.de/people-snapshot) for SMPL initialization and texture extraction
- [SMPL](https://smpl.is.tue.mpg.de/) for Parametric Body Representation
- [IDR](https://github.com/lioryariv/idr) for Neural Implicit Reconstruction
- [PyTorch3D](https://github.com/facebookresearch/pytorch3d) for Differential Explicit Rendering
This research was supported by National Natural Science Foundation of China (No. 62122071), the Youth Innovation Promotion Association CAS (No. 2018495), ``the Fundamental Research Funds for the Central Universities''(No. WK3470000021).
Citation
@inproceedings{jiang2022selfrecon,
author = {Boyi Jiang and Yang Hong and Hujun Bao and Juyong Zhang},
title = {SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Video},
booktitle = {{IEEE/CVF} Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022}
}
## License
For non-commercial research use only.