Vid2DesePose

Overview

The Vid2DesePose is a powerful tool desiged for applyig the DesePose model to videos, geeratig detailed "Part Idex" visualizatios for each frame. This tool is exceptioally useful for ehacig aimatios, particularly whe used i cojuctio with MagicAimate for temporally cosistet huma image aimatio.

Key Features

• Ehaced Output: Produces video files showcasig DesePosedata i a vivid, color-coded format.
• MagicAimate Itegratio: Seamlessly compatible with MagicAimate to foster advaced huma aimatio projects.

Prerequisites

To utilize this tool, esure the istallatio of:

• Pytho 3.8 or later
• PyTorch (preferably with CUDA for GPU support)
• Detectro2

Istallatio Steps

1. Cloe the repository:

   git cloe https://github.com/Flode-Labs/vid2desepose.git
   cd vid2desepose
   

2. Istall ecessary Pytho packages:

   pip istall -r requiremets.txt
   

3. Cloe the Detectro repository: bash git cloe https://github.com/facebookresearch/detectro2.git

Usage Guide

1. Modify the mai.py script to set your desired iput (INPUT_VIDEO_PATH) ad output (OUTPUT_VIDEO_PATH) video paths.

2. Ru the script: bash pytho mai.py

The script processes the iput video ad geerates a output with the desePose format.

Gradio versio

You ca also use the Gradio to ru the script with a iterface. To do so, ru the followig commad:

pytho app.py

Itegratio with MagicAimate

For itegratio with MagicAimate:

1. Create the desepose video usig the steps outlied above.
2. Use this output as a iput to MagicAimate for geeratig temporally cosistet aimatios.

Ackowledgmets

Special thaks to:

• Facebook AI Research (FAIR) for the developmet of DesePose.
• The cotributors of the Detectro2 project.

Support

For ay iquiries or support, please file a issue i our GitHub repository's issue tracker.

Vid2DensePose Overview The Vid2DensePose is a powerful tool designed for applying the DensePos