Face Dancer

Introduction

FaceDancer is a high-fidelity face-swapping model designed to handle pose variations and occlusions. It offers an advanced approach to face swapping, ensuring high-quality outputs even in challenging conditions. The project is accessible under a Creative Commons license and is part of ongoing research efforts in computer vision.

Architecture

FaceDancer employs a configuration known as Config C, which is detailed in the associated research paper. An additional model, FaceDancer_config_C_HQ, is trained specifically on high-resolution images, providing superior results for high-resolution inputs. The architecture is designed to maintain high fidelity in the swapped faces, particularly in challenging scenarios involving occlusions and varied poses.

Training

The training of FaceDancer involves using high-resolution images to enhance the detail and quality of face swaps. The models are fine-tuned to manage occlusions and pose variations, which are common challenges in face-swapping tasks. The exact details of the training process are available in the referenced paper, which provides insights into the datasets and methodologies used.

Guide: Running Locally

To run FaceDancer locally, follow these steps:

1. Clone the Repository: Access the source code from GitHub.

2. Install Dependencies: Ensure you have all necessary libraries and frameworks by following the repository's setup instructions.

3. Model Access: Request access to the models via the Hugging Face interface, agreeing to the terms of use.

4. Run the Script: Use the provided scripts to perform face swapping on images or videos.

5. Hardware Suggestions: For optimal performance, particularly with high-resolution models, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

FaceDancer is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (cc-by-nc-sa-4.0). This license allows for sharing and adaptation under specific terms: the model cannot be used commercially, and any derivatives must be shared alike. Proper attribution must be provided, and any use of the models should be cited as specified.