DeepFaceLab: Integrated, flexible and extensible face-swapping framework

TL;DR

DeepFaceLab is a flexible, user-friendly framework for high-quality face-swapping that supports customization and achieves cinema-level results, addressing workflow and performance issues in existing deepfake tools.

Contribution

It introduces a comprehensive, extensible face-swapping framework with a modular pipeline, enabling high-quality results and easy customization, surpassing prior methods in usability and performance.

Findings

Achieves cinema-quality face-swapping results

Offers a flexible, modular pipeline for customization

Outperforms existing face-swapping methods

Abstract

Deepfake defense not only requires the research of detection but also requires the efforts of generation methods. However, current deepfake methods suffer the effects of obscure workflow and poor performance. To solve this problem, we present DeepFaceLab, the current dominant deepfake framework for face-swapping. It provides the necessary tools as well as an easy-to-use way to conduct high-quality face-swapping. It also offers a flexible and loose coupling structure for people who need to strengthen their pipeline with other features without writing complicated boilerplate code. We detail the principles that drive the implementation of DeepFaceLab and introduce its pipeline, through which every aspect of the pipeline can be modified painlessly by users to achieve their customization purpose. It is noteworthy that DeepFaceLab could achieve cinema-quality results with high fidelity. We demonstrate the advantage of our system by comparing our approach with other face-swapping methods.For more information, please visit:https://github.com/iperov/DeepFaceLab/.