论文标题
用于属性转移的形状感知生成对抗网络
Shape-aware Generative Adversarial Networks for Attribute Transfer
论文作者
论文摘要
生成的对抗网络（GAN）已成功应用于在许多域（包括人脸图像的域）中传输视觉属性。这种成功部分归因于人脸具有相似形状的事实，而眼睛，鼻子和嘴巴的位置在不同的人中固定。当源和目标域共享不同形状时，属性转移更具挑战性。在本文中，我们引入了一种形状感知的GAN模型，该模型在传输属性时能够保持形状，并提出其在某些现实世界域中的应用。与其他基于最先进的图像到图像对图像翻译模型相比，我们建议的模型能够产生更具吸引力的结果，同时保持转移学习结果的质量。
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.
