---
license: other
pipeline_tag: image-to-video
library_name: diffusers
---

# MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance

This repository contains the model weights for **MimicMotion**, a controllable video generation framework proposed in the paper [MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance](https://huggingface.co/papers/2406.19680).

MimicMotion addresses significant challenges in video generation, such as controllability, video length, and richness of details. Our approach introduces several innovations:
-   **Confidence-aware pose guidance:** Ensures high frame quality and temporal smoothness.
-   **Regional loss amplification:** Significantly reduces image distortion based on pose confidence.
-   **Progressive latent fusion strategy:** Enables generation of arbitrary length videos with acceptable resource consumption.

With extensive experiments and user studies, MimicMotion demonstrates significant improvements over previous approaches in various aspects.

**[\ud83d\udcda Paper](https://huggingface.co/papers/2406.19680)** | **[\ud83c\udf10 Project Page](https://tencent.github.io/MimicMotion)** | **[\ud83d\udcbb GitHub Repo](https://github.com/Tencent/MimicMotion)**

<div align="center">
  <img src="https://huggingface.co/tencent/MimicMotion/resolve/main/assets/figures/model_structure.png" alt="MimicMotion Model Architecture" width="640"/>
  <br/>
  <i>An overview of the framework of MimicMotion.</i>
</div>

## Sample Usage

For the initial released version of the model checkpoint, it supports generating videos with a maximum of 72 frames at a 576x1024 resolution. If you encounter insufficient memory issues, you can appropriately reduce the number of frames.

### Environment setup

Recommend python 3+ with torch 2.x are validated with an Nvidia V100 GPU. Follow the command below to install all the dependencies of python:

```bash
conda env create -f environment.yaml
conda activate mimicmotion
```

### Download weights
If you experience connection issues with Hugging Face, you can utilize the mirror endpoint by setting the environment variable: `export HF_ENDPOINT=https://hf-mirror.com`.
Please download weights manually as follows:
```bash
cd MimicMotions/
mkdir models
```
1. Download DWPose pretrained model: [dwpose](https://huggingface.co/yzd-v/DWPose/tree/main)
    ```bash
    mkdir -p models/DWPose
    wget https://huggingface.co/yzd-v/DWPose/resolve/main/yolox_l.onnx?download=true -O models/DWPose/yolox_l.onnx
    wget https://huggingface.co/yzd-v/DWPose/resolve/main/dw-ll_ucoco_384.onnx?download=true -O models/DWPose/dw-ll_ucoco_384.onnx
    ```
2. Download the pre-trained checkpoint of MimicMotion from [Huggingface](https://huggingface.co/tencent/MimicMotion)
    ```bash
    wget -P models/ https://huggingface.co/tencent/MimicMotion/resolve/main/MimicMotion_1-1.pth
    ```
3. The SVD model [stabilityai/stable-video-diffusion-img2vid-xt-1-1](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt-1-1) will be automatically downloaded.

Finally, all the weights should be organized in `models` as follows:

```
models/
├── DWPose
│   ├── dw-ll_ucoco_384.onnx
│   └── yolox_l.onnx
└── MimicMotion_1-1.pth
```

### Model inference

A sample configuration for testing is provided as `test.yaml`. You can also easily modify the various configurations according to your needs.

```bash
python inference.py --inference_config configs/test.yaml
```

Tips: if your GPU memory is limited, try set env `PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:256`.

## License

These model weights of MimicMotion are fine-tuned with the assistance of Stable Video Diffusion (SVD) Powered by Stability AI. For detailed license information, please refer to [`LICENSE`](https://huggingface.co/tencent/MimicMotion/blob/main/LICENSE) and [`NOTICE`](https://huggingface.co/tencent/MimicMotion/blob/main/NOTICE) files.

## Citation

```bibtex
@inproceedings{zhang2025mimicmotion,
  title={MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance},
  author={Yuang Zhang and Jiaxi Gu and Li-Wen Wang and Han Wang and Junqi Cheng and Yuefeng Zhu and Fangyuan Zou},
  booktitle={International Conference on Machine Learning},
  year={2025}
}
```