MyTimeMachine: Personalized Facial Age Transformation
Authors: name: Luchao Qi, name: Jiaye Wu, name: Bang Gong, name: Annie N. Wang, name: David W. Jacobs, name: Roni Sengupta

Facial aging is a complex process, highly dependent on multiple factors like
gender, ethnicity, lifestyle, etc., making it extremely challenging to learn a
global aging prior to predict aging for any individual accurately. Existing
techniques often produce realistic and plausible aging results, but the re-aged
images often do not resemble the person's appearance at the target age and thus
need personalization. In many practical applications of virtual aging, e.g. VFX
in movies and TV shows, access to a personal photo collection of the user
depicting aging in a small time interval (20sim40 years) is often available.
However, naive attempts to personalize global aging techniques on personal
photo collections often fail. Thus, we propose MyTimeMachine (MyTM), which
combines a global aging prior with a personal photo collection (using as few as
50 images) to learn a personalized age transformation. We introduce a novel
Adapter Network that combines personalized aging features with global aging
features and generates a re-aged image with StyleGAN2. We also introduce three
loss functions to personalize the Adapter Network with personalized aging loss,
extrapolation regularization, and adaptive w-norm regularization. Our approach
can also be extended to videos, achieving high-quality, identity-preserving,
and temporally consistent aging effects that resemble actual appearances at
target ages, demonstrating its superiority over state-of-the-art approaches.