Deepfacelab: Integrated, flexible and extensible face-swapping framework

Kunlin Liu, Ivan Perov, Daiheng Gao, Nikolay Chervoniy, Wenbo Zhou, Weiming Zhang

Abstract: Highlights•Recent researches on face swapping methods focus on adjusting the identity extraction methods on one-shot scenarios. However, no matter how these models are adjusted, the identity information is always obtained from one shot source image. In other words, the identity information these methods leverage are guessed by a prior model. To achieve better performance, we develop an integrated, flexible and extensible framework, DeepFaceLab, to conduct cinema-level face-swapping with multi-shot data.•To make our framework practical, we invente a lot of auxiliary tools in each steps of DeepFaceLab. We develop a lot of human-in-the-loop designes in the whole framework. For examples, DeepFaceLab can swap any area of faces and conduct high-quality face-swapping even under heavy occlusion with the help of few-shot human labeling and XSeg. There are also dozens of image editing algorithms available in the mergence phase to generate satisfactory results.•DeepFaceLab is the leading software for creating deepfakes. As an open-source project, DeepFaceLab has obtained more than 35,000 stars on Github.