It seems that I updated a wrong version checkpoint, which may not work as well as in paper. In fact, the original project has been modified to a new one, and I'm still working on it. It's very struggling to rewrite and roll back to the old version (the version in this repository). So I'll update the new version code in this repositary as soon as the new paper is completed. By then, you can get a model that is similar to the old version by changing some parameters.
News
07/01/2023the code fordata preprocessingis updated.20/11/2022Thespeed_test.pyis added.pip install git+https://github.com/tatsy/torchmcubes.gitfor gpu marching cubes. We implement a faster mcubes, and I'm still cleaning the code.06/11/2022The code is released. But it's not complete. I'm still updating it.
FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction
Qiao Feng, Yebin Liu, Yu-Kun Lai, Jingyu Yang, Kun Li NeurIPS 2022
We plan to release the training and testing code of FOF in this repository as soon as possible. Any discussions or questions would be welcome!
To run this code, the following packages need to be installed.
numpy
pytorch
cv2
lmdb
numba
skimage
tqdm
pip install git+https://github.com/tatsy/torchmcubes.git
You can download the pretrained model and put it into the ckpt/base directory.
Download: pretrained model of FOF-base
The input images should be .pngs with 512*512 resolution in RGBA format. And the alpha channel is the mask.
A new version with cosine series(make the code neater)
Code for generating the training data
FOF-smpl
If you find this code useful for your research, please use the following BibTeX entry.
@inproceedings{li2022neurips,
author = {Qiao Feng and Yebin Liu and Yu-Kun Lai and Jingyu Yang and Kun Li},
title = {FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction},
booktitle = {NeurIPS},
year={2022},
}