EchoMimic: Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditioning

📣 📣 Updates

Gallery

Audio Driven (Sing)

Audio Driven (English)

Audio Driven (Chinese)

Landmark Driven

Audio + Selected Landmark Driven

（Some demo images above are sourced from image websites. If there is any infringement, we will immediately remove them and apologize.）

Installation

Download the Codes

git clone https://github.com/BadToBest/EchoMimic
  cd EchoMimic

Python Environment Setup

• Tested System Environment: Centos 7.2/Ubuntu 22.04, Cuda >= 11.7
• Tested GPUs: A100(80G) / RTX4090D (24G) / V100(16G)
• Tested Python Version: 3.8 / 3.10 / 3.11

conda create -n echomimic python=3.8
  conda activate echomimic

Install packages with pip

pip install -r requirements.txt

Download ffmpeg-static

export FFMPEG_PATH=/path/to/ffmpeg-4.4-amd64-static

Download pretrained weights

git lfs install
git clone https://huggingface.co/BadToBest/EchoMimic pretrained_weights

The pretrained_weights is organized as follows.

./pretrained_weights/
├── denoising_unet.pth
├── reference_unet.pth
├── motion_module.pth
├── face_locator.pth
├── sd-vae-ft-mse
│   └── ...
├── sd-image-variations-diffusers
│   └── ...
└── audio_processor
    └── whisper_tiny.pt

In which denoising_unet.pth / reference_unet.pth / motion_module.pth / face_locator.pth are the main checkpoints of EchoMimic. Other models in this hub can be also downloaded from it's original hub, thanks to their brilliant works:

• sd-vae-ft-mse
• sd-image-variations-diffusers
• audio_processor(whisper)

Audio-Drived Algo Inference

python -u infer_audio2vid.py
  python -u infer_audio2vid_pose.py

Audio-Drived Algo Inference On Your Own Cases

Edit the inference config file ./configs/prompts/animation.yaml, and add your own case:

test_cases:
  "path/to/your/image":
    - "path/to/your/audio"

The run the python inference script:

python -u infer_audio2vid.py

Motion Alignment between Ref. Img. and Driven Vid.

(Firstly download the checkpoints with '_pose.pth' postfix from huggingface)

Edit driver_video and ref_image to your path in demo_motion_sync.py, then run

python -u demo_motion_sync.py

Audio&Pose-Drived Algo Inference

python -u infer_audio2vid_pose.py

Pose-Drived Algo Inference

python -u infer_audio2vid_pose.py

Run the Gradio UI

Thanks to the contribution from @Robin021:

python -u webgui.py --server_port=3000

Release Plans

| Status | Milestone | ETA | |:--------:|:-------------------------------------------------------------------------|:--:| | ✅ | The inference source code of the Audio-Driven algo meet everyone on GitHub | 9th July, 2024 | | ✅ | Pretrained models trained on English and Mandarin Chinese to be released | 9th July, 2024 | | ✅ | The inference source code of the Pose-Driven algo meet everyone on GitHub | 13th July, 2024 | | ✅ | Pretrained models with better pose control to be released | 13th July, 2024 | | ✅ | Accelerated models to be released | 17th July, 2024 | | 🚀 | Pretrained models with better sing performance to be released | TBD | | 🚀 | Large-Scale and High-resolution Chinese-Based Talking Head Dataset | TBD |

Acknowledgements

We would like to thank the contributors to the AnimateDiff, Moore-AnimateAnyone and MuseTalk repositories, for their open research and exploration.

We are also grateful to V-Express and hallo for their outstanding work in the area of diffusion-based talking heads.

If we missed any open-source projects or related articles, we would like to complement the acknowledgement of this specific work immediately.

Citation

If you find our work useful for your research, please consider citing the paper :

@misc{chen2024echomimic,
  title={EchoMimic: Lifelike Audio-Driven Portrait Animations through Editable Landmark Conditioning},
  author={Zhiyuan Chen, Jiajiong Cao, Zhiquan Chen, Yuming Li, Chenguang Ma},
  year={2024},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

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