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DTSTAMP:20240214T070243Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T173000
DTEND;TZID=Australia/Melbourne:20231212T174000
UID:siggraphasia_SIGGRAPH Asia 2023_sess162_papers_249@linklings.com
SUMMARY:Repurposing Diffusion Inpainters for Novel View Synthesis
DESCRIPTION:Technical Papers\n\nYash Kant (University of Toronto, Snap Inc
 .); Aliaksandr Siarohin, Michael Vasilkovsky, Riza Alp Guler, Jian Ren, an
 d Sergey Tulyakov (Snap Inc.); and Igor Gilitschenski (University of Toron
 to)\n\nIn this paper, we present a method for generating consistent novel 
 views from a single source image. Our approach focuses on maximizing the r
 euse of visible pixels from the source view. To achieve this, we use a mon
 ocular depth estimator that transfers visible pixels from the source view 
 to the target view. Additionally, we leverage a learned object shape prior
  obtained through 2D inpainting diffusion. We also introduce a dedicated f
 ine-tuning technique using diffusion and a novel masking mechanism based o
 n epipolar lines to further improve the quality of our approach. We train 
 our method on the large-scale Objaverse dataset. This allows our framework
  to perform zero-shot novel view synthesis on a variety of objects. We eva
 luate the zero-shot abilities of our framework on three challenging datase
 ts: Google Scanned Objects, Ray Traced Multiview, and Common Objects in 3D
 .\n\nRegistration Category: Full Access\n\nSession Chair: Binh-Son Hua (Tr
 inity College Dublin)
URL:https://asia.siggraph.org/2023/full-program?id=papers_249&sess=sess162
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