High fidelity face attribute editing based on TransUNet

Lihao Liu; Qiong Zhang; Xiaokang Ren

doi:10.1117/12.3033649

13 June 2024 High fidelity face attribute editing based on TransUNet

Lihao Liu, Qiong Zhang, Xiaokang Ren

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131802W (2024) https://doi.org/10.1117/12.3033649
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

In the last few years,with the development of generative adversarial networks (GAN), Significant technical updates have been made in the field of face attribute editing. A new method is proposed in this paper for editing face attributes. Based on the advantages of StyleGAN in face generation, TransUNet and High-Fidelity Encoder are added to the entire network to achieve accurate, controllable and highly realistic editing effects. By integrating the powerful ability of TransUNet to extract image feature information, the structure and semantic information of face images can be accurately captured, and accurate attribute editing can be achieved. In addition, we have designed High-Fidelity Encoder that focuses on maintaining the quality of the visual effects and naturalness of the images during the editing process, minimizing visual artifacts and unnatural appearances, and producing highly realistic editing results that are indistinguishable from real photos. From the results obtained by our experiments, our method has significant advantages in attribute accuracy and visual effect quality.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lihao Liu, Qiong Zhang, and Xiaokang Ren "High fidelity face attribute editing based on TransUNet", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131802W (13 June 2024); https://doi.org/10.1117/12.3033649

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KEYWORDS

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Education and training

Image restoration

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Image segmentation

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