论文标题
DeepFacelab：集成，灵活和可扩展的面部扫地框架
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
论文作者
论文摘要
DeepFake防御不仅需要检测研究，而且还需要发电方法的努力。但是，当前的DeepFake方法遭受了晦涩的工作流程和性能不佳的影响。为了解决这个问题，我们提出了DeepFacelab，这是当前的主要深层框架框架。它提供了必要的工具以及一种易于使用的方法来进行高质量的面部汇总。它还为需要使用其他功能加强管道的人而无需编写复杂的样板代码，为那些需要加强管道的人提供了灵活而松散的耦合结构。我们详细介绍了推动DeepFacelab实施的原则并介绍其管道，用户可以通过痛苦地修改管道的各个方面，以实现其自定义目的。值得注意的是，DeepFacelab可以以高忠诚获得电影质量的结果。我们通过将我们的方法与其他面部交换方法进行比较来证明系统的优势。有关更多信息，请访问：https：//github.com/iperov/deepfacelab/。
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/.
