PA-GAN: Progressive Attention Generative Adversarial Network for Facial Attribute Editing (2007.05892v1)

Abstract: 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.

• The paper introduces PA-GAN, a model that uses a progressive attention mechanism for accurate facial attribute editing.
• It employs a multi-level encoder-decoder with residual learning to target specific regions and refine edits continuously.
• Experimental results on CelebA demonstrate high attribute correctness and superior preservation of non-target features compared to existing methods.

Progressive Attention GAN for Facial Attribute Editing

The paper introduces the Progressive Attention Generative Adversarial Network (PA-GAN), a model designed to improve the precision and quality of facial attribute editing. The paper addresses the typical compromise in existing methods between generating correct facial attributes and maintaining the preservation of unrelated facial features such as identity or background. This dilemma is tackled through a progressive attention mechanism within a GAN framework, offering an innovative approach to facial image editing.

Methodology

The PA-GAN employs a progressive attention strategy embedded in an encoder-decoder architecture, facilitating attribute editing from high to low feature levels. The essential concept is to utilize attention masks at each feature level to precisely delineate the area of attribute editing, ensuring minimal interference with irrelevant regions:

1. Progressive Editing: The method conducts attribute editing progressively across multiple levels of feature representation, beginning with coarse features at higher levels and refining details at lower levels. This progressive approach allows for granular control over the attribute generation process.
2. Attention Mechanism: At each level, an attention mask guides the editing process, ensuring edits remain confined to appropriate areas. This targeted approach significantly reduces unwanted changes in non-target areas such as background or facial identity markers.
3. Residual Learning: A residual strategy is implemented to refine the attention masks iteratively, which improves their precision and robustness as the feature resolution increases.
4. Multi-Attribute Support: The network is capable of editing multiple attributes simultaneously, where individual attention masks are calculated for each attribute. These masks are combined to accommodate complex editing requirements in a single model.

Experimental Results

Experimentation on the CelebA dataset demonstrated PA-GAN's superior performance in both generating high-accuracy attribute edits and preserving irrelevant facial features compared to existing models like StarGAN, AttGAN, and STGAN. It achieved high attribute correctness without sacrificing non-target area fidelity, a common shortcoming in previous methods.

Quantitative Metrics

• Attribute Editing Accuracy: PA-GAN maintained high levels of accuracy in generating specified attributes, comparable to or exceeding existing state-of-the-art models.
• Irrelevance Preservation Error: The innovation in attention-guided editing led to lower preservation errors, highlighting its capability to maintain nontarget details more effectively than competitor models.

Implications and Future Directions

The introduction of PA-GAN pushes the domain of facial attribute editing closer to practical, high-fidelity applications. Its ability to perform precise and correct edits with minimal undesired changes makes it highly applicable in areas such as digital photo editing, entertainment industry enhancements, and augmented reality systems. Additionally, the progressive attention mechanism may be explored beyond facial attributes to broader applications in image-to-image translation tasks across various domains. Future research may focus on optimizing computational efficiency, extending the model's utility to higher-resolution images, and refining the generalizability of PA-GAN to different types of image editing beyond faces.

In conclusion, PA-GAN represents a notable advancement in the nuanced field of facial attribute editing, establishing a new benchmark for accuracy and preservation in generative adversarial networks. Through meticulous attention-based processing, the paper demonstrates how complex generative tasks can benefit significantly from structured, progressive, and constrained editing approaches.