Controllable Face Aging

TL;DR

This paper introduces a controllable face aging method using an attribute disentanglement GAN that allows fine-grained control over aging effects while preserving key facial attributes like race and gender.

Contribution

It proposes a novel attribute disentanglement GAN framework enabling explicit control over facial attributes during aging, improving flexibility over existing methods.

Findings

Achieves comparable aging effects to state-of-the-art methods.

Provides enhanced control over facial attribute manipulation.

Demonstrates effectiveness on benchmark datasets.

Abstract

Motivated by the following two observations: 1) people are aging differently under different conditions for changeable facial attributes, e.g., skin color may become darker when working outside, and 2) it needs to keep some unchanged facial attributes during the aging process, e.g., race and gender, we propose a controllable face aging method via attribute disentanglement generative adversarial network. To offer fine control over the synthesized face images, first, an individual embedding of the face is directly learned from an image that contains the desired facial attribute. Second, since the image may contain other unwanted attributes, an attribute disentanglement network is used to separate the individual embedding and learn the common embedding that contains information about the face attribute (e.g., race). With the common embedding, we can manipulate the generated face image with the desired attribute in an explicit manner. Experimental results on two common benchmarks demonstrate that our proposed generator achieves comparable performance on the aging effect with state-of-the-art baselines while gaining more flexibility for attribute control. Code is available at supplementary material.