The Vid2DensePose is a powerful tool designed for applying the DensePose model to videos, generating detailed "Part Index" visualizations for each frame. This tool is exceptionally useful for enhancing animations, particularly when used in conjunction with MagicAnimate for temporally consistent human image animation.

• Enhanced Output: Produces video files showcasing DensePosedata in a vivid, color-coded format.
• MagicAnimate Integration: Seamlessly compatible with MagicAnimate to foster advanced human animation projects.

To utilize this tool, ensure the installation of:

• Python 3.8 or later
• PyTorch (preferably with CUDA for GPU support)
• Detectron2

1. Clone the repository:

   git clone https://github.com/Flode-Labs/vid2densepose.git
   cd vid2densepose

2. Install necessary Python packages:

   pip install -r requirements.txt

3. Clone the Detectron repository:

   git clone https://github.com/facebookresearch/detectron2.git

Run the script:

python main.py -i sample_videos/input_video.mp4 -o sample_videos/output_video.mp4

The script processes the input video and generates an output with the densePose format.

You can also use the Gradio to run the script with an interface. To do so, run the following command:

python app.py

For integration with MagicAnimate:

1. Create the densepose video using the steps outlined above.
2. Use this output as an input to MagicAnimate for generating temporally consistent animations.

Special thanks to:

• Facebook AI Research (FAIR) for the development of DensePose.
• The contributors of the Detectron2 project.
• Gonzalo Vidal for the sample videos.
• Sylvain Filoni for the deployment of the Gradio Space in Hugging Face.

For any inquiries or support, please file an issue in our GitHub repository's issue tracker.