PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing
This addresses the challenge of precise facial attribute editing for applications in image manipulation and computer vision, representing an incremental improvement over prior methods.
The paper tackles the problem of facial attribute editing, where existing methods compromise between correct attribute generation and preserving other details like identity and background, by proposing a progressive attention GAN (PA-GAN) that progressively edits attributes from high to low feature levels using attention masks, resulting in better preservation of irrelevant details compared to state-of-the-art methods.
Facial attribute editing aims to manipulate attributes on the human face, e.g., adding a mustache or changing the hair color. Existing approaches suffer from a serious compromise between correct attribute generation and preservation of the other information such as identity and background, because they edit the attributes in the imprecise area. To resolve this dilemma, we propose a progressive attention GAN (PA-GAN) for facial attribute editing. In our approach, the editing is progressively conducted from high to low feature level while being constrained inside a proper attribute area by an attention mask at each level. This manner prevents undesired modifications to the irrelevant regions from the beginning, and then the network can focus more on correctly generating the attributes within a proper boundary at each level. As a result, our approach achieves correct attribute editing with irrelevant details much better preserved compared with the state-of-the-arts. Codes are released at https://github.com/LynnHo/PA-GAN-Tensorflow.