A new face swap method for image and video domains: a technical report
- URL: http://arxiv.org/abs/2202.03046v1
- Date: Mon, 7 Feb 2022 10:15:50 GMT
- Title: A new face swap method for image and video domains: a technical report
- Authors: Daniil Chesakov, Anastasia Maltseva, Alexander Groshev, Andrey
Kuznetsov, Denis Dimitrov
- Abstract summary: We introduce a new face swap pipeline that is based on FaceShifter architecture.
New eye loss function, super-resolution block, and Gaussian-based face mask generation leads to improvements in quality.
- Score: 60.47144478048589
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deep fake technology became a hot field of research in the last few years.
Researchers investigate sophisticated Generative Adversarial Networks (GAN),
autoencoders, and other approaches to establish precise and robust algorithms
for face swapping. Achieved results show that the deep fake unsupervised
synthesis task has problems in terms of the visual quality of generated data.
These problems usually lead to high fake detection accuracy when an expert
analyzes them. The first problem is that existing image-to-image approaches do
not consider video domain specificity and frame-by-frame processing leads to
face jittering and other clearly visible distortions. Another problem is the
generated data resolution, which is low for many existing methods due to high
computational complexity. The third problem appears when the source face has
larger proportions (like bigger cheeks), and after replacement it becomes
visible on the face border. Our main goal was to develop such an approach that
could solve these problems and outperform existing solutions on a number of
clue metrics. We introduce a new face swap pipeline that is based on
FaceShifter architecture and fixes the problems stated above. With a new eye
loss function, super-resolution block, and Gaussian-based face mask generation
leads to improvements in quality which is confirmed during evaluation.
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