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/.
翻译:深度防守不仅需要探测研究,而且需要培养方法。然而,目前的深层防守方法受到模糊的工作流程和不良性能的影响。为了解决这一问题,我们介绍了当前主要的面部冲洗深海假框架DeepFaceLab。它提供了必要的工具以及便于使用的进行高质量面部冲洗的方法。它还为那些需要用其他特性加强管道而不撰写复杂锅炉代码的人提供了一个灵活和松散的联结结构。我们详细介绍了驱动DeepFaceLab实施和引入管道的原则,通过这些原则,用户可以不费力地修改管道的每个方面,以实现其定制目的。值得注意的是,深层法西拉布可以非常忠实地取得电影质量的成果。我们通过将我们的方法与其他面部擦拭方法进行比较来展示我们的系统优势。关于更多信息,请访问:https://github.com/iperov/DeepFaceLab/。