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:20240214T070241Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_964@linklings.com
SUMMARY:Face0: Instantaneously Conditioning a Text-to-Image Model on a Fac
 e
DESCRIPTION:Technical Papers\n\nDani Valevski, Danny Lumen, Yossi Matias, 
 and Yaniv Leviathan (Google Research)\n\nWe present Face0, a novel way to 
 instantaneously condition a text-to-image generation model on a face, in s
 ample time, without any optimization procedures such as fine-tuning or inv
 ersions. We augment a dataset of annotated images with embeddings of the i
 ncluded faces and train an image generation model, on the augmented datase
 t. Once trained, our system is practically identical at inference time to 
 the underlying base model, and is therefore able to generate images, given
  a user-supplied face image and a prompt, in just a couple of seconds. Our
  method achieves pleasing results, is remarkably simple, extremely fast, a
 nd equips the underlying model with new capabilities, like controlling the
  generated images both via text or via direct manipulation of the input fa
 ce embeddings. In addition, when using a fixed random vector instead of a 
 face embedding from a user supplied image, our method essentially solves t
 he problem of consistent character generation across images. Finally, whil
 e requiring further research, we hope that our method, which decouples the
  model’s textual biases from its biases on faces, might be a step towards 
 some mitigation of biases in future text-to-image models.\n\nRegistration 
 Category: Full Access, Enhanced Access, Trade Exhibitor, Experience Hall E
 xhibitor
URL:https://asia.siggraph.org/2023/full-program?id=papers_964&sess=sess209
END:VEVENT
END:VCALENDAR