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

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

2023 IEEE International Conference on Image Processing (ICIP)

10.1109/ICIP49359.2023.10223056

http://dx.doi.org/10.1109/icip49359.2023.10223056

qiyu wei, WEIHUA ZHENG, Yuxin Li, Zhongyao Cheng, Zeng Zeng, Xulei Yang

11 September 2023

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

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.

Publisher Copyright

There was no specific funding for the research done

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https://oar.a-star.edu.sg/communities-collections/articles/19405

978-1-7281-9835-4

Institute for Infocomm Research