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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2005.05535 |
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| _version_ | 1866909302599450624 |
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| author | Perov, Ivan Gao, Daiheng Chervoniy, Nikolay Liu, Kunlin Marangonda, Sugasa Umé, Chris Dpfks Facenheim, Carl Shift RP, Luis Jiang, Jian Zhang, Sheng Wu, Pingyu Zhou, Bo Zhang, Weiming |
| author_facet | Perov, Ivan Gao, Daiheng Chervoniy, Nikolay Liu, Kunlin Marangonda, Sugasa Umé, Chris Dpfks Facenheim, Carl Shift RP, Luis Jiang, Jian Zhang, Sheng Wu, Pingyu Zhou, Bo Zhang, Weiming |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2005_05535 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | DeepFaceLab: Integrated, flexible and extensible face-swapping framework Perov, Ivan Gao, Daiheng Chervoniy, Nikolay Liu, Kunlin Marangonda, Sugasa Umé, Chris Dpfks Facenheim, Carl Shift RP, Luis Jiang, Jian Zhang, Sheng Wu, Pingyu Zhou, Bo Zhang, Weiming Computer Vision and Pattern Recognition Machine Learning Multimedia Image and Video Processing 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/. |
| title | DeepFaceLab: Integrated, flexible and extensible face-swapping framework |
| topic | Computer Vision and Pattern Recognition Machine Learning Multimedia Image and Video Processing |
| url | https://arxiv.org/abs/2005.05535 |