Temporal Optimization for Face Swapping Video based on Consistency Inheritance
Pages 165 - 170
Abstract
Applying existing face swapping algorithms independently to each video frame typically leads to temporal inconsistency. We analyze the inconsistency in the generated results and model inter-frame inconsistency as time-domain noise. We propose a face swapping mapper network to inherit identity and suppress noise. Training strategies include primary perceptual loss to learn the face swapping information of the reference face, optical flow loss to impose temporal constraints, and identity loss to transfer identity information. In addition, we introduce a 3D face disentanglement model to regress FLAME parameters and guide the optimization direction precisely for facial detail consistency. Only a pair of original and swapped videos is used for training, eliminating the need for a large dataset. Experiments demonstrate that we improve the temporal consistency and detail consistency of the results, and enhance the generation quality of face swapping methods at the video level.
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Index Terms
- Temporal Optimization for Face Swapping Video based on Consistency Inheritance
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Published In
July 2024
261 pages
ISBN:9798400710117
DOI:10.1145/3674399
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 30 July 2024
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- Research
- Refereed limited
Funding Sources
- the Natural Science Foundation of China
- Key Research and De- velopment program of Anhui Province
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ACM-TURC '24
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