DeepFaceLab：集成化、灵活且可扩展的换脸框架
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
AI总结：
本文提出当前主流的换脸框架DeepFaceLab，通过灵活松耦合的流程设计实现高质量、电影级换脸效果，并支持用户无痛定制，解决了现有deepfake方法工作流程不清晰和性能不佳的问题。
摘要：
Deepfake防御不仅需要检测方面的研究，也需要生成方法的努力。然而，当前的deepfake方法存在工作流程不清晰和性能不佳的问题。为解决这一问题，我们提出了DeepFaceLab，这是当前用于换脸的主导deepfake框架。它提供了必要的工具以及一种易于使用的方式来进行高质量换脸。它还为那些需要用其他功能强化其流程而无需编写复杂样板代码的人提供了灵活且松耦合的结构。我们详细阐述了驱动DeepFaceLab实现的原则，并介绍了其流程，通过该流程，用户可以对流程的每个方面进行无痛修改以实现定制目的。值得注意的是，DeepFaceLab能够实现电影级质量且高保真的结果。我们通过将我们的方法与其他换脸方法进行比较，展示了我们系统的优势。欲了解更多信息，请访问：https://github.com/iperov/DeepFaceLab/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
