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DTSTAMP:20260114T163644Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T143000
DTEND;TZID=Australia/Melbourne:20231214T144000
UID:siggraphasia_SIGGRAPH Asia 2023_sess132_papers_407@linklings.com
SUMMARY:MyStyle++: A Controllable Personalized Generative Prior
DESCRIPTION:Libing Zeng (Texas A&M University), Lele Chen and Yi Xu (OPPO 
 US Research Center), and Nima Kalantari (Texas A&M University)\n\nIn this 
 paper, we propose an approach to obtain a personalized generative prior wi
 th explicit control over a set of attributes. We build upon MyStyle, a rec
 ently introduced method, that tunes the weights of a pre-trained StyleGAN 
 face generator on a few images of an individual. This system allows synthe
 sizing, editing, and enhancing images of the target individual with high f
 idelity to their facial features. However, it relies on the latent space o
 f StyleGAN, and thus inherits its limitations in controllability. We propo
 se to address this problem through a novel optimization system that organi
 zes the latent space in addition to tuning the generator. Our key contribu
 tion is to formulate a loss that arranges the latent codes, corresponding 
 to the input images, along a set of specific directions according to their
  attribute. We demonstrate that our approach, dubbed MyStyle++, is able to
  synthesize, edit, and enhance images of an individual with great control 
 over the attributes, while preserving the unique facial characteristics of
  that individual.\n\nRegistration Category: Full Access\n\nSession Chair: 
 Jun-Yan Zhu (Carnegie Mellon University)\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_407&sess=sess132
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