StyleAU: StyleGAN based Facial Action Unit Manipulation for Expression Editing

Facial expression editing has a wide range of applications, such as emotion detection, human-computer interaction, and social entertainment. However, existing expression editing methods either fail to allow for fine-grained editing, resulting in unnatural and unrealistic facial expressions, or gener...

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Bibliographic Details
Main Authors: Guo, Yanliang, Hou, Xianxu, Liu, Feng, Shen, Linlin, Wang, Lei, Wang, Zhen, Liu, Peng
Format: Conference Proceeding
Language:English
Subjects:
Faces
Feature extraction
Gold
Human computer interaction
Image quality
Image reconstruction
Process control
Online Access:Request full text
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Summary:Facial expression editing has a wide range of applications, such as emotion detection, human-computer interaction, and social entertainment. However, existing expression editing methods either fail to allow for fine-grained editing, resulting in unnatural and unrealistic facial expressions, or generate artifacts and blurs, leading to poor image quality. In this paper, we propose a novel framework called StyleAU, which is based on StyleGAN and facial action units, to address these problems. Our framework leverages the pre-trained StyleGAN prior knowledge to enable action unit editing of the face in the StyleGAN latent space, allowing precise expression editing. In addition, we use an encoder to extract multi-scale content features to achieve high-fidelity image reconstruction. Our approach qualitatively and quantitatively outperforms competing methods for action unit manipulation and expression editing.
ISSN:2474-9699
DOI:10.1109/IJCB57857.2023.10448612