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

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Controlling Facial Attribute Synthesis by Disentangling Attribute Feature Axes in Latent Space
Title:
Controlling Facial Attribute Synthesis by Disentangling Attribute Feature Axes in Latent Space
Journal Title:
2023 IEEE International Conference on Image Processing (ICIP)
Keywords:
Publication Date:
11 September 2023
Citation:
Wei, Q., Zheng, W., Li, Y., Cheng, Z., Zeng, Z., & Yang, X. (2023). Controlling Facial Attribute Synthesis by Disentangling Attribute Feature Axes in Latent Space. 2023 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip49359.2023.10223056
Abstract:
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.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISBN:
978-1-7281-9835-4
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