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DTSTAMP:20260114T163650Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T142500
DTEND;TZID=Australia/Melbourne:20231213T143500
UID:siggraphasia_SIGGRAPH Asia 2023_sess128_papers_532@linklings.com
SUMMARY:DreamEditor: Text-Driven 3D Scene Editing with Neural Fields
DESCRIPTION:Jingyu Zhuang (Sun Yat-sen University); Chen Wang (University 
 of Pennsylvania, Tsinghua University); Liang Lin (Sun Yat-sen University);
  Lingjie Liu (University of Pennsylvania); and Guanbin Li (Sun Yat-sen Uni
 versity)\n\nNeural fields have achieved impressive advancements in view sy
 nthesis and scene reconstruction. However, editing these neural fields rem
 ains challenging due to the implicit encoding of geometry and texture info
 rmation. In this paper, we propose DreamEditor, a novel framework that ena
 bles users to perform controlled editing of neural fields using text promp
 ts. By representing scenes as mesh-based neural fields, DreamEditor allows
  localized editing within specific regions. DreamEditor utilizes the text 
 encoder of a pretrained text-to-Image diffusion model to automatically ide
 ntify the regions to be edited based on the semantics of the text prompts.
  Subsequently, DreamEditor optimizes the edit region and aligns its geomet
 ry and texture with the text prompts through score distillation sampling. 
 Extensive experiments have demonstrated that DreamEditor can accurately ed
 it neural fields of real-world scenes according to the given text prompts 
 while ensuring consistency in irrelevant areas. DreamEditor generates high
 ly realistic textures and geometry, significantly surpassing previous work
 s in both quantitative and qualitative evaluations.\n\nRegistration Catego
 ry: Full Access\n\nSession Chair: Jianfei Cai (Monash University)\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_532&sess=sess128
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