Paper | Project page | Demo video
Official implementation of "Make Encoder Great Again in 3D GAN Inversion through Geometry and Occlusion-Aware Encoding" ICCV 2023
We present a encoder-based 3D generative adversarial network (GAN) inversion framework that can efficiently synthesize photo-realistic novel views while preserving geometry and details of the input image.
cd goae
conda create --name goae python=3.8
conda activate goae
pip install -r requirements.txt
Dataset preparation can refer to EG3D or these codes
The pretrained model checkpoint can be downloaded from google drive, Put those checkpoint into the directory GOAE/pretrained
. Note that current pretrained AFA only modifies the triplane on 32*32 resolution, more higher resolution modify can achieve better result.
You can use the command below to test the example.
python infer.py --multi_view --video
You can use the command below to edit the example.
python infer.py --multi_view --video --edit --edit_attr glass --alpha 1.0
Training codes can be downloaded from here.
If you find this work useful for your research, please cite:
@article{yuan2023make,
title={Make Encoder Great Again in 3D GAN Inversion through Geometry and Occlusion-Aware Encoding},
author={Yuan, Ziyang and Zhu, Yiming and Li, Yu and Liu, Hongyu and Yuan, Chun},
journal={arXiv preprint arXiv:2303.12326},
year={2023}
}
If you have any comments or questions, please open a new issue or feel free to contact Ziyang Yuan (yuanzy22@mails.tsinghua.edu).