BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Australia/Melbourne
X-LIC-LOCATION:Australia/Melbourne
BEGIN:DAYLIGHT
TZOFFSETFROM:+1000
TZOFFSETTO:+1100
TZNAME:AEDT
DTSTART:19721003T020000
RRULE:FREQ=YEARLY;BYMONTH=4;BYDAY=1SU
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:19721003T020000
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
TZNAME:AEST
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260114T163707Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231215T101500
DTEND;TZID=Australia/Melbourne:20231215T111500
UID:siggraphasia_SIGGRAPH Asia 2023_sess135@linklings.com
SUMMARY:Text To Anything
DESCRIPTION:Text-Guided Synthesis of Eulerian Cinemagraphs\n\nWe introduce
  Text2Cinemagraph,  a fully automated method for creating cinemagraphs fro
 m text descriptions - an especially challenging task when prompts feature 
 imaginary elements and artistic styles, given the complexity of interpreti
 ng the semantics and motions of these images. We focus on cinemagr...\n\n\
 nAniruddha Mahapatra (Carnegie Mellon University); Aliaksandr Siarohin, Hs
 in-Ying Lee, and Sergey Tulyakov (Snap Inc.); and Jun-Yan Zhu (Carnegie Me
 llon University)\n---------------------\nBreak-A-Scene: Extracting Multipl
 e Concepts from a Single Image\n\nText-to-image model personalization aims
  to introduce a user-provided concept to the model, allowing its synthesis
  in diverse contexts. However, current methods primarily focus on the case
  of learning a single concept from multiple images with variations in back
 grounds and poses, and struggle when a...\n\n\nOmri Avrahami (The Hebrew U
 niversity of Jerusalem), Kfir Aberman (Google Research), Ohad Fried (Reich
 man University), Daniel Cohen-Or (Tel Aviv University), and Dani Lischinsk
 i (The Hebrew University of Jerusalem)\n---------------------\nCLIP-Guided
  StyleGAN Inversion for Text-Driven Real Image Editing\n\nResearchers have
  recently begun exploring the use of StyleGAN-based models for real image 
 editing. One particularly interesting application is using natural languag
 e descriptions to guide the editing process. Existing approaches for editi
 ng images using language either resort to instance-level laten...\n\n\nAbd
 ul Basit Anees and Ahmet Canberk Baykal (Koç University), Duygu Ceylan (Ad
 obe Research), Erkut Erdem (Hacettepe University), and Aykut Erdem and Den
 iz Yuret (Koç University)\n---------------------\nRerender A Video: Zero-S
 hot Text-Guided Video-to-Video Translation\n\nLarge text-to-image diffusio
 n models have exhibited impressive proficiency in generating high-quality 
 images. However, when applying these models to video domain, ensuring temp
 oral consistency across video frames remains a formidable challenge.\nThis
  paper proposes a novel zero-shot text-guided video...\n\n\nShuai Yang, Yi
 fan Zhou, Ziwei Liu, and Chen Change Loy (Nanyang Technological University
 , Singapore)\n---------------------\nFace0: Instantaneously Conditioning a
  Text-to-Image Model on a Face\n\nWe present Face0, a novel way to instant
 aneously condition a text-to-image generation model on a face, in sample t
 ime, without any optimization procedures such as fine-tuning or inversions
 . We augment a dataset of annotated images with embeddings of the included
  faces and train an image generation m...\n\n\nDani Valevski, Danny Lumen,
  Yossi Matias, and Yaniv Leviathan (Google Research)\n\nRegistration Categ
 ory: Full Access\n\nSession Chair: Chongyang Ma (ByteDance)
END:VEVENT
END:VCALENDAR