title: MyTimeMachine: Personalized Facial Age Transformation
summary: 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.
paper_authors._id: 6744115b33e10fc6d427c915
paper_authors.name: Luchao Qi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-25T21:11:02.893Z
paper_authors._id: 635734a5fe4ffe942db442b2
paper_authors.avatarUrl: /avatars/34780e375254e109bff5ee9d3f43f5bd.svg
paper_authors.fullname: Luchao Qi
paper_authors.type: user
paper_authors.user: luchaoqi
paper_authors._id: 6744115b33e10fc6d427c916
paper_authors.name: Jiaye Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-25T21:11:08.790Z
paper_authors._id: 62f263202a73f342df1776c9
paper_authors.avatarUrl: /avatars/359d61d5ff93466746beca133b4d908b.svg
paper_authors.fullname: Jiaye Wu
paper_authors.type: user
paper_authors.user: wujiaye
paper_authors._id: 6744115b33e10fc6d427c917
paper_authors.name: Bang Gong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-25T21:11:14.053Z
paper_authors._id: 65277e72ccaa9d9bfa4c04c9
paper_authors.avatarUrl: /avatars/5f35666250de88bff2dd6cfc33f40ce8.svg
paper_authors.fullname: Bang Gong
paper_authors.type: user
paper_authors.user: gongbang
paper_authors._id: 6744115b33e10fc6d427c918
paper_authors.name: Annie N. Wang
paper_authors._id: 6744115b33e10fc6d427c919
paper_authors.name: David W. Jacobs
paper_authors._id: 6744115b33e10fc6d427c91a
paper_authors.name: Roni Sengupta
paper_title: MyTimeMachine: Personalized Facial Age Transformation
paper_summary: 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.
paper_ai_keywords: Adapter Network StyleGAN2 personalized aging loss extrapolation regularization adaptive w-norm regularization identity-preserving temporally consistent