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DTSTAMP:20240214T070248Z
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:Technical Papers\n\nLibing Zeng (Texas A&M University), Lele C
 hen and Yi Xu (OPPO US Research Center), and Nima Kalantari (Texas A&M Uni
 versity)\n\nIn this paper, we propose an approach to obtain a personalized
  generative prior with explicit control over a set of attributes. We build
  upon MyStyle, a recently introduced method, that tunes the weights of a p
 re-trained StyleGAN face generator on a few images of an individual. This 
 system allows synthesizing, editing, and enhancing images of the target in
 dividual with high fidelity to their facial features. However, it relies o
 n the latent space of StyleGAN, and thus inherits its limitations in contr
 ollability. We propose to address this problem through a novel optimizatio
 n system that organizes the latent space in addition to tuning the generat
 or. Our key contribution is to formulate a loss that arranges the latent c
 odes, corresponding to the input images, along a set of specific direction
 s according to their attribute. We demonstrate that our approach, dubbed M
 yStyle++, is able to synthesize, edit, and enhance images of an individual
  with great control over the attributes, while preserving the unique facia
 l characteristics of that individual.\n\nRegistration Category: Full Acces
 s\n\nSession Chair: Jun-Yan Zhu (Carnegie Mellon University)
URL:https://asia.siggraph.org/2023/full-program?id=papers_407&sess=sess132
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