A3GAN: An Attribute-aware Attentive Generative Adversarial Network for Face Aging

TL;DR

A3GAN is a novel face aging model that uses attribute-awareness and attention mechanisms to produce realistic aged faces while preserving identity and attributes, outperforming existing methods.

Contribution

The paper introduces A3GAN, which integrates facial attributes and attention mechanisms into GANs for improved face aging synthesis.

Findings

Achieves state-of-the-art performance on face aging datasets.

Produces photorealistic aged face images with preserved identity.

Effectively focuses modifications on age-related facial regions.

Abstract

Face aging, which aims at aesthetically rendering a given face to predict its future appearance, has received significant research attention in recent years. Although great progress has been achieved with the success of Generative Adversarial Networks (GANs) in synthesizing realistic images, most existing GAN-based face aging methods have two main problems: 1) unnatural changes of high-level semantic information (e.g. facial attributes) due to the insufficient utilization of prior knowledge of input faces, and 2) distortions of low-level image content including ghosting artifacts and modifications in age-irrelevant regions. In this paper, we introduce A3GAN, an Attribute-Aware Attentive face aging model to address the above issues. Facial attribute vectors are regarded as the conditional information and embedded into both the generator and discriminator, encouraging synthesized faces to be faithful to attributes of corresponding inputs. To improve the visual fidelity of generation results, we leverage the attention mechanism to restrict modifications to age-related areas and preserve image details. Moreover, the wavelet packet transform is employed to capture textural features at multiple scales in the frequency space. Extensive experimental results demonstrate the effectiveness of our model in synthesizing photorealistic aged face images and achieving state-of-the-art performance on popular face aging datasets.