./pretrained_models/
|-- champ
|   |-- denoising_unet.pth
|   |-- guidance_encoder_depth.pth
|   |-- guidance_encoder_dwpose.pth
|   |-- guidance_encoder_normal.pth
|   |-- guidance_encoder_semantic_map.pth
|   |-- reference_unet.pth
|   `-- motion_module.pth
|-- image_encoder
|   |-- config.json
|   `-- pytorch_model.bin
|-- sd-vae-ft-mse
|   |-- config.json
|   |-- diffusion_pytorch_model.bin
|   `-- diffusion_pytorch_model.safetensors
`-- stable-diffusion-v1-5
    |-- feature_extractor
    |   `-- preprocessor_config.json
    |-- model_index.json
    |-- unet
    |   |-- config.json
    |   `-- diffusion_pytorch_model.bin
    `-- v1-inference.yaml

We have provided several sets of example data for inference. Please first download and place them in the example_data folder.

Here is the command for inference:

  python inference.py --config configs/inference.yaml

Animation results will be saved in results folder. You can change the reference image or the guidance motion by modifying inference.yaml.

You can also extract the driving motion from any videos and then render with Blender. We will later provide the instructions and scripts for this.

Note: The default motion-01 in inference.yaml has more than 500 frames and takes about 36GB VRAM. If you encounter VRAM issues, consider switching to other example data with less frames.

We thank the authors of MagicAnimate, Animate Anyone, and AnimateDiff for their excellent work. Our project is built upon Moore-AnimateAnyone, and we are grateful for their open-source contributions.

Visit our roadmap to preview the future of Champ.

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

@misc{zhu2024champ,
      title={Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance},
      author={Shenhao Zhu and Junming Leo Chen and Zuozhuo Dai and Yinghui Xu and Xun Cao and Yao Yao and Hao Zhu and Siyu Zhu},
      year={2024},
      eprint={2403.14781},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}