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MimicTalk: Mimicking a personalized and expressive 3D talking face in few minutes | NeurIPS 2024

This is the official repo of MimicTalk with Pytorch implementation, for training a personalized and expressive talking avatar in minutes. The code is built upon our previous work, Real3D-Portrait (ICLR 2024), which is a one-shot NeRF-based talking avatar system and enables the fast training and good quality of our MimicTalk. You can visit our Demo Page for watching demo videos, and read our Paper for technical details.

Quick Start!

Environment Installation

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

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 & MimicTalk

Download Pre-trained MimicTalk Checkpoints：Google Drive or BaiduYun Disk with Password mimi

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

checkpoints/
├── mimictalk_orig
│   └── os_secc2plane_torso
│       ├── config.yaml
│       └── model_ckpt_steps_100000.ckpt
|-- 240112_icl_audio2secc_vox2_cmlr
│     ├── config.yaml
│     └── model_ckpt_steps_1856000.ckpt
└── pretrained_ckpts
    └── mit_b0.pth

checkpoints_mimictalk/
└── German_20s
    ├── config.yaml
    └── model_ckpt_steps_10000.ckpt

Train & Infer MimicTalk in two lines

python inference/train_mimictalk_on_a_video.py # train the model, this may take 10 minutes for 2,000 steps
python inference/mimictalk_infer.py # infer the model

Detailed options for train & infer

Currently, we provide CLI, Gradio WebUI for inference. We support Audio-Driven talking head generation for specific-person (which is from torso_ckpt), and at least prepare driving audio for inference. Optionly, providing style video enables model to predict corressponding talking style with it.

Firstly, switch to project folder and activate conda environment:

cd <mimictalkRoot>
conda activate mimictalk
export PYTHONPATH=./
export HF_ENDPOINT=https://hf-mirror.com

Gradio WebUI

Run Gradio WebUI demo, upload resouces in webpage，click Training button to train a person-specific MimicTalk model, and then click Generate button to inference with arbitary audio and style：

python inference/app_mimictalk.py

CLI Training for specific-person video

Provide source video for specific-person:

python inference/train_mimictalk_on_a_video.py \
--video_id <PATH_TO_SOURCE_VIDEO> \
--max_updates <UPDATES_NUMBER> \
--work_dir <PATH_TO_SAVING_CKPT>

Some training optional parameters:

--torso_ckpt Pre-trained Real3d-Portrait checkpoints path
--max_updates The number of training updates.
--batch_size Batch size during training: 1 needs about 8GB VRAM; 2 needs about 15GB
--lr_triplane Learning rate of triplane: for video, 0.1; for an image, 0.001
--work_dir When not assigned, the results will be stored at checkpoints_mimictalk/.

Commandline example:

python inference/train_mimictalk_on_a_video.py \
--video_id data/raw/videos/German_20s.mp4 \
--max_updates 2000 \
--work_dir checkpoints_mimictalk/German_20s

CLI Inference

Provide driving audio and driving style (Optionly):

python inference/mimictalk_infer.py \
--drv_aud <PATH_TO_AUDIO> \
--drv_style <PATH_TO_STYLE_VIDEO, OPTIONAL> \
--drv_pose <PATH_TO_POSE_VIDEO, OPTIONAL> \
--bg_img <PATH_TO_BACKGROUND_IMAGE, OPTIONAL> \
--out_name <PATH_TO_OUTPUT_VIDEO, OPTIONAL>

Some inference 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
--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/mimictalk_infer.py \
--drv_aud data/raw/examples/Obama_5s.wav \
--drv_pose data/raw/examples/German_20s.mp4 \
--drv_style data/raw/examples/German_20s.mp4 \
--bg_img data/raw/examples/bg.png \
--out_name output.mp4 \
--out_mode final

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:

@inproceedings{ye2024mimicktalk,
    author    = {Ye, Zhenhui and Zhong, Tianyun and Ren, Yi and Yang, Jiaqi and Li, Weichuang and Huang, Jiangwei and Jiang, Ziyue and He, Jinzheng and Huang, Rongjie and Liu, Jinglin and Zhang, Chen and Yin, Xiang and Ma, Zejun and Zhao, Zhou},
    title     = {MimicTalk: Mimicking a personalized and expressive 3D talking face in few minutes},
    journal   = {NeurIPS},
    year      = {2024},
}
@inproceedings{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  = {ICLR},
  year={2024}
}