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GeneFace++: Generalized and Stable Real-Time 3D Talking Face Generation
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This is the official implementation of GeneFace++ Paper with Pytorch, which enables high lip-sync, high video-reality and high system-efficiency 3D talking face generation. You can visit our Demo Page to watch demo videos and learn more details.
<p align="center"> <br> <img src="assets/geneface++.png" width="100%"/> <br> </p>🔥MimicTalk Released
We have released the code of MimicTalk (https://github.com/yerfor/MimicTalk/), which is a SOTA NeRF-based person-specific talking face method and achieves better visual quality and enables talking style control.
You may also interested in
- We release Real3D-portrait (ICLR 2024 Spotlight), (https://github.com/yerfor/Real3DPortrait), a NeRF-based one-shot talking face system. Only upload one image and enjoy realistic talking face!
Quick Start!
We provide a guide for a quick start in GeneFace++.
-
Step 1: Follow the steps in
docs/prepare_env/install_guide.md
, create a new python environment namedgeneface
, and download 3DMM files intodeep_3drecib/BFM
. -
Step 2: Download pre-processed dataset of May(Google Drive or BaiduYun Disk with password 98n4), and place it here
data/binary/videos/May/trainval_dataset.npy
-
Step 3: Download pre-trained audio-to-motino model
audio2motion_vae.zip
(Google Drive or BaiduYun Disk with password 9cqp) and motion-to-video checkpointmotion2video_nerf.zip
, which is specific to May (in this Google Drive or in thisBaiduYun Disk with password 98n4), and unzip them to./checkpoints/
After these steps,your directories checkpoints
and data
should be like this:
> checkpoints
> audio2motion_vae
> motion2video_nerf
> may_head
> may_torso
> data
> binary
> videos
> May
trainval_dataset.npy
- Step 4: activate
geneface
Python environment, and execute:
export PYTHONPATH=./
python inference/genefacepp_infer.py --a2m_ckpt=checkpoints/audio2motion_vae --head_ckpt= --torso_ckpt=checkpoints/motion2video_nerf/may_torso --drv_aud=data/raw/val_wavs/MacronSpeech.wav --out_name=may_demo.mp4
Or you can play with our Gradio WebUI:
export PYTHONPATH=./
python inference/app_genefacepp.py --a2m_ckpt=checkpoints/audio2motion_vae --head_ckpt= --torso_ckpt=checkpoints/motion2video_nerf/may_torso
Or use our provided Google Colab and run all cells in it.
Train GeneFace++ with your own videos
Please refer to details in docs/process_data
and docs/train_and_infer
.
Below are answers to frequently asked questions when training GeneFace++ on custom videos:
- Please make sure that the head segment occupies a relatively large region in the video (e.g., similar to the provided
May.mp4
). Or you need to hand-crop your training video. issue - Make sure that the talking person appears in every frame of the video, otherwise the data preprocessing pipeline may be failed.
- We only tested our code on Liunx (Ubuntu/CentOS). It is welcome that someone who are willing to share their installation guide on Windows/MacOS.
ToDo
- Release Inference Code of Audio2Motion and Motion2Video.
- Release Pre-trained weights of Audio2Motion and Motion2Video.
- Release Training Code of Motino2Video Renderer.
- Release Gradio Demo.
- Release Google Colab.
- **Release Training Code of Audio2Motion and Post-Net. (Maybe 2024.06.01) **
Citation
If you found this repo helpful to your work, please consider cite us:
@article{ye2023geneface,
title={GeneFace: Generalized and High-Fidelity Audio-Driven 3D Talking Face Synthesis},
author={Ye, Zhenhui and Jiang, Ziyue and Ren, Yi and Liu, Jinglin and He, Jinzheng and Zhao, Zhou},
journal={arXiv preprint arXiv:2301.13430},
year={2023}
}
@article{ye2023geneface++,
title={GeneFace++: Generalized and Stable Real-Time Audio-Driven 3D Talking Face Generation},
author={Ye, Zhenhui and He, Jinzheng and Jiang, Ziyue and Huang, Rongjie and Huang, Jiawei and Liu, Jinglin and Ren, Yi and Yin, Xiang and Ma, Zejun and Zhao, Zhou},
journal={arXiv preprint arXiv:2305.00787},
year={2023}
}