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BEGIN:VEVENT
DTSTAMP:20260114T163641Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T140000
DTEND;TZID=Australia/Melbourne:20231214T150000
UID:siggraphasia_SIGGRAPH Asia 2023_sess132@linklings.com
SUMMARY:Personalized Generative Models
DESCRIPTION:A Neural Space-Time Representation for Text-to-Image Personali
 zation\n\nA key aspect of text-to-image personalization methods is the man
 ner in which the target concept is represented within the generative proce
 ss. This choice greatly affects the visual fidelity, downstream editabilit
 y, and disk space needed to store the learned concept. In this paper, we e
 xplore a new t...\n\n\nYuval Alaluf, Elad Richardson, Gal Metzer, and Dani
 el Cohen-Or (Tel Aviv University)\n---------------------\nDomain-Agnostic 
 Tuning-Encoder for Fast Personalization of Text-To-Image Models\n\nText-to
 -image (T2I) personalization allows users to guide the creative image gene
 ration process by combining their own visual concepts in natural language 
 prompts. \nRecently, encoder-based techniques have emerged as a new effect
 ive approach for T2I personalization, reducing the need for multiple ima..
 .\n\n\nMoab Arar (Tel-Aviv University); Rinon Gal (Tel Aviv University, NV
 IDIA Research); Yuval Atzmon (NVIDIA Research); Gal Chechik (NVIDIA Resear
 ch, Bar-Ilan University); Daniel Cohen-Or (Tel Aviv University); Ariel Sha
 mir (Reichman University (IDC)); and Amit H. Bermano (Tel Aviv University)
 \n---------------------\nContent-based Search for Deep Generative Models\n
 \nThe growing proliferation of customized and pretrained generative models
  has made it infeasible for a user to be fully cognizant of every model in
  existence. To address this need, we introduce the task of content-based m
 odel search: given a query and a large set of generative models, finding t
 he mod...\n\n\nDaohan Lu, Sheng-Yu Wang, Nupur Kumari, Rohan Agarwal, and 
 Mia Tang (Carnegie Mellon University); David Bau (Northeastern University)
 ; and Jun-Yan Zhu (Carnegie Mellon University)\n---------------------\nMyS
 tyle++: A Controllable Personalized Generative Prior\n\nIn this paper, we 
 propose an approach to obtain a personalized generative prior with explici
 t control over a set of attributes. We build upon MyStyle, a recently intr
 oduced method, that tunes the weights of a pre-trained StyleGAN face gener
 ator on a few images of an individual. This system allows sy...\n\n\nLibin
 g Zeng (Texas A&M University), Lele Chen and Yi Xu (OPPO US Research Cente
 r), and Nima Kalantari (Texas A&M University)\n---------------------\nProS
 pect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Mod
 els\n\nPersonalizing generative models offers a way to guide image generat
 ion with user-provided references. Current personalization methods can inv
 ert an object or concept into the textual conditioning space and compose n
 ew natural sentences for text-to-image diffusion models. However, represen
 ting and ed...\n\n\nYuxin Zhang (MAIS, Institute of Automation, Chinese Ac
 ademy of Sciences; School of Artificial Intelligence, University of Chines
 e Academy of Sciences); Weiming Dong (MAIS, Institute of Automation, Chine
 se Academy of Sciences; School of AI,University of Chinese Academy of Scie
 nces); Fan Tang (Institute of Computing Technology, Chinese Academy of Sci
 ences); Nisha Huang (School of AI,University of Chinese Academy of Science
 s; MAIS, Institute of Automation, Chinese Academy of Sciences); Haibin Hua
 ng and Chongyang Ma (Kuaishou Technology); Tong-Yee Lee (National Cheng-Ku
 ng University); Oliver Deussen (University of Konstanz); and Changsheng Xu
  (MAIS, Institute of Automation, Chinese Academy of Sciences; School of Ar
 tificial Intelligence, University of Chinese Academy of Sciences)\n\nRegis
 tration Category: Full Access\n\nSession Chair: Jun-Yan Zhu (Carnegie Mell
 on University)
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