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DTSTART:18871231T000000
DTSTART:19881009T020000
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BEGIN:VEVENT
DTSTAMP:20230103T035311Z
LOCATION:Room 325-AB\, Level 3\, West Wing
DTSTART;TZID=Asia/Seoul:20221208T110000
DTEND;TZID=Asia/Seoul:20221208T123000
UID:siggraphasia_SIGGRAPH Asia 2022_sess168_papers_273@linklings.com
SUMMARY:CLIP-Mesh: Generating textured meshes from text using pretrained i
 mage-text models
DESCRIPTION:Technical Papers\n\nCLIP-Mesh: Generating textured meshes from
  text using pretrained image-text models\n\nMohammad Khalid, Xie, Belilovs
 ky, Popa\n\nWe present a technique for zero-shot generation of a 3D model 
 using only a target text prompt. Without any 3D supervision our method def
 orms the control shape of a limit subdivided surface along with its textur
 e map and normal map to obtain a 3D asset that corresponds to the input te
 xt prompt and can be easily deployed into games or modeling applications. 
 We rely only on a pre-trained CLIP model that compares the input text prom
 pt with differentiably rendered images of our 3D model. While previous wor
 ks have focused on stylization or required training of generative models w
 e perform optimization on mesh parameters directly to generate shape, text
 ure or both. To constrain the optimization to produce plausible meshes and
  textures we introduce a number of techniques using image augmentations an
 d the use of a pretrained prior that generates CLIP image embeddings given
  a text embedding.\n\nRegistration Category: FULL ACCESS, ON-DEMAND ACCESS
 \n\nLanguage: ENGLISH\n\nFormat: IN-PERSON, ON-DEMAND
URL:https://sa2022.siggraph.org/en/full-program/?id=papers_273&sess=sess16
 8
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