Controllable Face Aging
This work addresses the need for more precise and customizable face aging in applications like entertainment or forensics, but it is incremental as it builds on existing generative adversarial networks with added attribute disentanglement.
The paper tackles the problem of controllable face aging by proposing a method that allows fine control over facial attributes during aging, such as keeping race and gender unchanged while adjusting others like skin color. Experimental results show the method achieves comparable aging performance to state-of-the-art baselines while offering more flexibility in attribute control.
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.