Controllable semantic image editing enables a user to change entire image
attributes with a few clicks, e.g., gradually making a summer scene look like
it was taken in winter. Classic approaches for this task use a Generative
Adversarial Net (GAN) to learn a latent space and suitable latent-space
transformations. However, current approaches often suffer from attribute edits
that are entangled, global image identity changes, and diminished
photo-realism. To address these concerns, we learn multiple attribute
transformations simultaneously, integrate attribute regression into the
training of transformation functions, and apply a content loss and an
adversarial loss that encourages the maintenance of image identity and
photo-realism. We propose quantitative evaluation strategies for measuring
controllable editing performance, unlike prior work, which primarily focuses on
qualitative evaluation. Our model permits better control for both single- and
multiple-attribute editing while preserving image identity and realism during
transformation. We provide empirical results for both natural and synthetic
images, highlighting that our model achieves state-of-the-art performance for
targeted image manipulation.

本研究提出了一种新的控制图像属性的编辑方法，通过学习多种属性变换和应用内容损失和对抗损失来实现单一和多属性的编辑，并保留图像身份和真实感。此外，提出了客观评价标准以衡量模型性能，结果表明该模型在目标图像操作方面达到了最先进水平。