Paper ID: 2203.12985 • Published Mar 24, 2022
Learning Disentangled Representation for One-shot Progressive Face Swapping
Qi Li, Weining Wang, Chengzhong Xu, Zhenan Sun, Ming-Hsuan Yang
TL;DR
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Although face swapping has attracted much attention in recent years, it
remains a challenging problem. Existing methods leverage a large number of data
samples to explore the intrinsic properties of face swapping without
considering the semantic information of face images. Moreover, the
representation of the identity information tends to be fixed, leading to
suboptimal face swapping. In this paper, we present a simple yet efficient
method named FaceSwapper, for one-shot face swapping based on Generative
Adversarial Networks. Our method consists of a disentangled representation
module and a semantic-guided fusion module. The disentangled representation
module comprises an attribute encoder and an identity encoder, which aims to
achieve the disentanglement of the identity and attribute information. The
identity encoder is more flexible, and the attribute encoder contains more
attribute details than its competitors. Benefiting from the disentangled
representation, FaceSwapper can swap face images progressively. In addition,
semantic information is introduced into the semantic-guided fusion module to
control the swapped region and model the pose and expression more accurately.
Experimental results show that our method achieves state-of-the-art results on
benchmark datasets with fewer training samples. Our code is publicly available
at this https URL