# GeneFacePlusPlus **Repository Path**: jasonlp/GeneFacePlusPlus ## Basic Information - **Project Name**: GeneFacePlusPlus - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2024-12-09 - **Last Updated**: 2024-12-09 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # GeneFace++: Generalized and Stable Real-Time 3D Talking Face Generation [![arXiv](https://img.shields.io/badge/arXiv-Paper-%3CCOLOR%3E.svg)](https://arxiv.org/abs/2305.00787)| [![GitHub Stars](https://img.shields.io/github/stars/yerfor/GeneFacePlusPlus)](https://github.com/yerfor/GeneFacePlusPlus) | [![downloads](https://img.shields.io/github/downloads/yerfor/GeneFacePlusPlus/total.svg)](https://github.com/yerfor/GeneFacePlusPlus/releases) | ![visitors](https://visitor-badge.glitch.me/badge?page_id=yerfor/GeneFacePlusPlus) [English Readme](./README.md) 这个仓库是GeneFace++的官方PyTorch实现,用于实现高嘴形对齐(lip-sync)、高视频真实度(video reality)、高系统效率(system efficiency)的虚拟人视频合成。您可以访问我们的[项目页面](https://genefaceplusplus.github.io/)以观看Demo视频, 阅读我们的[论文](https://arxiv.org/abs/2301.13430)以了解技术细节。



## 您可能同样感兴趣 - 我们发布了Real3D-portrait (ICLR 2024 Spotlight), ([https://github.com/yerfor/Real3DPortrait](https://github.com/yerfor/Real3DPortrait)), 一个基于NeRF的单图驱动说话人合成系统, 仅需上传一张照片即可合成真实的说话人视频! ## 快速上手! 我们在这里提供一个最快体验GeneFace++的流程。 - 步骤1:根据我们在`docs/prepare_env/install_guide.md`中的步骤,新建一个名为`geneface`的Python环境,并下载所需的3DMM文件。 - 步骤2:下载预处理好的May的数据集 `trainval_dataset.npy` ([Google Drive](https://drive.google.com/drive/folders/1SwZ7uRa5ESzzq_Cd21-Lk5heAZxa9oZO?usp=sharing) 或 [BaiduYun Disk](https://pan.baidu.com/s/1U_FalVoxgb9sAb9FD1cZEw?pwd=98n4) 提取码: 98n4), 放置在`data/binary/videos/May/trainval_dataset.npy`路径下。 - 步骤3:下载预训练好的通用的audio-to-motino模型 `audio2motion_vae.zip` ([Google Drive](https://drive.google.com/drive/folders/1M6CQH52lG_yZj7oCMaepn3Qsvb-8W2pT?usp=sharing) 或 [BaiduYun Disk](https://pan.baidu.com/s/1U_FalVoxgb9sAb9FD1cZEw?pwd=98n4) 提取码: 98n4) 和专用于May的motion-to-video模型 `motion2video_nerf.zip` ([Google Drive](https://drive.google.com/drive/folders/1M6CQH52lG_yZj7oCMaepn3Qsvb-8W2pT?usp=sharing) 或 [BaiduYun Disk](https://pan.baidu.com/s/1U_FalVoxgb9sAb9FD1cZEw?pwd=98n4) 提取码: 98n4), 解压到`./checkpoints/`目录下。 做完上面的步骤后,您的 `checkpoints`和`data` 文件夹的结构应该是这样的: ```shell > checkpoints > audio2motion_vae > motion2video_nerf > may_head > may_torso > data > binary > videos > May trainval_dataset.npy ``` - 步骤4: 激活`geneface`的Python环境,然后执行: ```bash 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 ``` - 或者可以使用我们提供的Gradio WebUI: ```bash export PYTHONPATH=./ python inference/app_genefacepp.py --a2m_ckpt=checkpoints/audio2motion_vae --head_ckpt= --torso_ckpt=checkpoints/motion2video_nerf/may_torso ``` - 抑或可以使用我们提供的[Google Colab](https://colab.research.google.com/github/yerfor/GeneFacePlusPlus/blob/main/inference/genefacepp_demo.ipynb),运行其中的所有cell。 ## 在自己的视频上训练GeneFace++ 如果您想在您自己的目标人物视频上训练GeneFace++,请遵循 `docs/process_data`和`docs/train_and_infer/`中的步骤。 ## ToDo - [x] **Release Inference Code of Audio2Motion and Motion2Video.** - [x] **Release Pre-trained weights of Audio2Motion and Motion2Video.** - [x] **Release Training Code of Motino2Video Renderer.** - [x] **Release Gradio Demo.** - [x] **Release Google Colab.** - [ ] **Release Training Code of Audio2Motion and Post-Net.** ## 引用 如果这个仓库对你有帮助,请考虑引用我们的工作: ``` @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} } ```