Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis | ICLR 2024 Spotlight

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This is the official repo of Real3D-Portrait with Pytorch implementation, for one-shot and high video reality talking portrait synthesis. You can visit our Demo Page for watching demo videos, and read our Paper for technical details.

🔥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.

🔥 Update

• [2024.07.02] We release the training code of the whole system, including audio-to-motion model, image-to-plane model, secc2plane model, and the secc2plane_torso model, please refer to docs/train_models. We also release the code to preprocess and binarize the dataset, please refer to docs/process_data. Thanks for your patience!

You may also interested in

• We release the code of GeneFace++, (https://github.com/yerfor/GeneFacePlusPlus), a NeRF-based person-specific talking face system, which aims at producing high-quality talking face videos with extreme idenetity-similarity of the target person.

Quick Start!

Environment Installation

Please refer to Installation Guide, prepare a Conda environment real3dportrait.

Download Pre-trained & Third-Party Models

3DMM BFM Model

Download 3DMM BFM Model from Google Drive or BaiduYun Disk with Password m9q5.

Put all the files in deep_3drecon/BFM, the file structure will be like this:

deep_3drecon/BFM/
├── 01_MorphableModel.mat
├── BFM_exp_idx.mat
├── BFM_front_idx.mat
├── BFM_model_front.mat
├── Exp_Pca.bin
├── facemodel_info.mat
├── index_mp468_from_mesh35709.npy
└── std_exp.txt

Pre-trained Real3D-Portrait

Download Pre-trained Real3D-Portrait：Google Drive or BaiduYun Disk with Password 6x4f

Put the zip files in checkpoints and unzip them, the file structure will be like this:

checkpoints/
├── 240210_real3dportrait_orig
│   ├── audio2secc_vae
│   │   ├── config.yaml
│   │   └── model_ckpt_steps_400000.ckpt
│   └── secc2plane_torso_orig
│       ├── config.yaml
│       └── model_ckpt_steps_100000.ckpt
└── pretrained_ckpts
    └── mit_b0.pth

Inference

Currently, we provide CLI, Gradio WebUI and Google Colab for inference. We support both Audio-Driven and Video-Driven methods:

• For audio-driven, at least prepare source image and driving audio
• For video-driven, at least prepare source image and driving expression video

Gradio WebUI

Run Gradio WebUI demo, upload resouces in webpage，click Generate button to inference：

python inference/app_real3dportrait.py

Google Colab

Run all the cells in this Colab.

CLI Inference

Firstly, switch to project folder and activate conda environment:

cd <Real3DPortraitRoot>
conda activate real3dportrait
export PYTHONPATH=./

For audio-driven, provide source image and driving audio:

python inference/real3d_infer.py \
--src_img <PATH_TO_SOURCE_IMAGE> \
--drv_aud <PATH_TO_AUDIO> \
--drv_pose <PATH_TO_POSE_VIDEO, OPTIONAL> \
--bg_img <PATH_TO_BACKGROUND_IMAGE, OPTIONAL> \
--out_name <PATH_TO_OUTPUT_VIDEO, OPTIONAL>

For video-driven, provide source image and driving expression video(as --drv_aud parameter):

python inference/real3d_infer.py \
--src_img <PATH_TO_SOURCE_IMAGE> \
--drv_aud <PATH_TO_EXP_VIDEO> \
--drv_pose <PATH_TO_POSE_VIDEO, OPTIONAL> \
--bg_img <PATH_TO_BACKGROUND_IMAGE, OPTIONAL> \
--out_name <PATH_TO_OUTPUT_VIDEO, OPTIONAL>

Some optional parameters：

• --drv_pose provide motion pose information, default to be static poses
• --bg_img provide background information, default to be image extracted from source
• --mouth_amp mouth amplitude, higher value leads to wider mouth
• --map_to_init_pose when set to True, the initial pose will be mapped to source pose, and other poses will be equally transformed
• --temperature stands for the sampling temperature of audio2motion, higher for more diverse results at the expense of lower accuracy
• --out_name When not assigned, the results will be stored at infer_out/tmp/.
• --out_mode When final, only outputs the final result; when concat_debug, also outputs visualization of several intermediate process.

Commandline example:

python inference/real3d_infer.py \
--src_img data/raw/examples/Macron.png \
--drv_aud data/raw/examples/Obama_5s.wav \
--drv_pose data/raw/examples/May_5s.mp4 \
--bg_img data/raw/examples/bg.png \
--out_name output.mp4 \
--out_mode concat_debug

ToDo

• Release Pre-trained weights of Real3D-Portrait.
• Release Inference Code of Real3D-Portrait.
• Release Gradio Demo of Real3D-Portrait..
• Release Google Colab of Real3D-Portrait..
• Release Training Code of Real3D-Portrait.

Disclaimer

Any organization or individual is prohibited from using any technology mentioned in this paper to generate someone's talking video without his/her consent, including but not limited to government leaders, political figures, and celebrities. If you do not comply with this item, you could be in violation of copyright laws.

Citation

If you found this repo helpful to your work, please consider cite us:

@article{ye2024real3d,
  title={Real3D-Portrait: One-shot Realistic 3D Talking Portrait Synthesis},
  author={Ye, Zhenhui and Zhong, Tianyun and Ren, Yi and Yang, Jiaqi and Li, Weichuang and Huang, Jiawei and Jiang, Ziyue and He, Jinzheng and Huang, Rongjie and Liu, Jinglin and others},
  journal={arXiv preprint arXiv:2401.08503},
  year={2024}
}
@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}
}
@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}
}