2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDSS-MMSDF-1.3
Paper Title FSFT-NET: FACE TRANSFER VIDEO GENERATION WITH FEW-SHOT VIEWS
Authors Luchuan Song, Guojun Yin, Bin Liu, Unversity of Science and Technology of China, China; Yuhui Zhang, University of Science and Technology of China, China; Nenghai Yu, Unversity of Science and Technology of China, China
SessionSS-MMSDF-1: Special Session: AI for Multimedia Security and Deepfake 1
LocationArea B
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic Special Sessions: Artificial Intelligence for Multimedia Security and Deepfake
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract To transfer head pose and expression with few photographs is a novel yet challenging task in deepfake generation. Despite impressive results have been achieved in related works, there are still two limitations in the existing methods: 1) most of the methods are based on computer graphics, which take a lot of computing resources,while lacking of generalization for different identity, 2) few-shot based methods cannot handle the few-shot style transfer video generation. To address these distortion problems, we propose a novel deep learning framework, named as Few Shot Face Transfer Networks(FSFT-Net) which works for the face transfer video generation. The proposed FSFT-Net driven by arbitrary portrait video involves a cascaded-based style generator to synthesize stable video with few free-view images. In addition, the frame and video discriminators are adopted for optimization of the proposed generator. The FSFT-Net performs long-term adversarial training on large-scale video datasets. Extensive experiments demonstrate that our FSFT-Net outperforms state-of-the-art methods both quantitatively and qualitatively results.