MyTimeMachine: Personalized Facial Age Transformation
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.
6744115b33e10fc6d427c915
Luchao Qi
admin_assigned
2024-11-25T21:11:02.893Z
635734a5fe4ffe942db442b2
/avatars/9da256875e6cfc780c6420e465c49099.svg
Luchao Qi
user
luchaoqi
6744115b33e10fc6d427c916
Jiaye Wu
admin_assigned
2024-11-25T21:11:08.790Z
62f263202a73f342df1776c9
/avatars/359d61d5ff93466746beca133b4d908b.svg
Jiaye Wu
user
wujiaye
6744115b33e10fc6d427c917
Bang Gong
admin_assigned
2024-11-25T21:11:14.053Z
65277e72ccaa9d9bfa4c04c9
/avatars/5f35666250de88bff2dd6cfc33f40ce8.svg
Bang Gong
user
gongbang
6744115b33e10fc6d427c918
Annie N. Wang
6744115b33e10fc6d427c919
David W. Jacobs
6744115b33e10fc6d427c91a
Roni Sengupta
MyTimeMachine: Personalized Facial Age Transformation
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.
MyTimeMachine combines global aging models with personal photo collections to generate realistic, identity-preserving aging images and videos.
Adapter Network StyleGAN2 personalized aging loss extrapolation regularization adaptive w-norm regularization