Abstract:
Deep neural network-based methods were proposed for
face attribute manipulation. There still exist, however, two
major issues, i.e., insufficient visual quality (or resolution)
of the results and lack of user control. They limit the applicability of existing methods since users may have different
editing preference on facial attributes. In this paper, we
address these issues by proposing a semantic component
model. The model decomposes a facial attribute into multiple semantic components, each corresponds to a specific
face region. This not only allows for user control of edit
strength on different parts based on their preference, but
also makes it effective to remove unwanted edit effect. Further, each semantic component is composed of two fundamental elements, which determine the edit effect and region
respectively. This property provides fine interactive control.
As shown in experiments, our model not only produces highquality results, but also allows effective user interaction
Authors: Ying-Cong Chen, Xiaohui Shen, Zhe Lin, Xin Lu,
I-Ming Pao, Jiaya Jia
