Controlling Facial Attribute Synthesis by Disentangling Attribute Feature Axes in Latent Space

In this study, we propose a novel approach to synthesize high-resolution and hyper-realistic face images with controlled attributes. Firstly, by training an attribute classifier to assign attribute labels to given synthesized face images, we build the links between latent vectors and face attributes...

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Bibliographic Details
Published in2023 IEEE International Conference on Image Processing (ICIP) pp. 346 - 350
Main Authors Wei, Qiyu, Zheng, Weihua, Li, Yuxin, Cheng, Zhongyao, Zeng, Zeng, Yang, Xulei
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.10.2023
Subjects
Aerospace electronics
Face Attribute
Face Image Synthesis
Faces
Facial features
Gram-Schmidt orthogonalization
Image synthesis
Latent Vector
Linear algebra
Numerical analysis
Training
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Summary:In this study, we propose a novel approach to synthesize high-resolution and hyper-realistic face images with controlled attributes. Firstly, by training an attribute classifier to assign attribute labels to given synthesized face images, we build the links between latent vectors and face attributes. Secondly, we adapt the regression method to match the distributions of latent vectors with the corresponding face attributes, to control the attribute synthesis in the face images. Finally, we use the Gram-Schmidt orthogonalization algorithm to disentangle the attribute feature axes in latent space, such that a change in one attribute will not cause any changes in other attributes. Extensive experiments demonstrate the effectiveness of the proposed approach for high-quality face image synthesis with controlled attributes.
DOI:10.1109/ICIP49359.2023.10223056