Shape-aware generative adversarial networks for attribute transfer

Lei Luo; William Hsu; Shangxian Wang

doi:10.1117/12.2586992

4 January 2021 Shape-aware generative adversarial networks for attribute transfer

Lei Luo, William Hsu, Shangxian Wang

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 1160515 (2021) https://doi.org/10.1117/12.2586992
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

Generative adversarial networks (GANs) have been successfully applied to transfer visual attributes in many domains, including that of human face images. This success is partly attributable to the facts that human faces have similar shapes and the positions of eyes, noses, and mouths are fixed among different people. Attribute transfer is more challenging when the source and target domain share different shapes. In this paper, we introduce a shape-aware GAN model that is able to preserve shape when transferring attributes, and propose its application to some real-world domains. Compared to other state-of-art GANs-based image-to-image translation models, the model we propose is able to generate more visually appealing results while maintaining the quality of results from transfer learning.

Citation Download Citation

Lei Luo, William Hsu, and Shangxian Wang "Shape-aware generative adversarial networks for attribute transfer", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 1160515 (4 January 2021); https://doi.org/10.1117/12.2586992

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