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DTSTAMP:20260114T163642Z
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:Yash Kant (University of Toronto, Snap Inc.); Aliaksandr Siaro
 hin, Michael Vasilkovsky, Riza Alp Guler, Jian Ren, and Sergey Tulyakov (S
 nap Inc.); and Igor Gilitschenski (University of Toronto)\n\nIn this paper
 , we present a method for generating consistent novel views from a single 
 source image. Our approach focuses on maximizing the reuse of visible pixe
 ls from the source view. To achieve this, we use a monocular depth estimat
 or 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 fine-tuning technique
  using diffusion and a novel masking mechanism based on epipolar lines to 
 further improve the quality of our approach. We train our method on the la
 rge-scale Objaverse dataset. This allows our framework to perform zero-sho
 t novel view synthesis on a variety of objects. We evaluate the zero-shot 
 abilities of our framework on three challenging datasets: Google Scanned O
 bjects, Ray Traced Multiview, and Common Objects in 3D.\n\nRegistration Ca
 tegory: Full Access\n\nSession Chair: Binh-Son Hua (Trinity College Dublin
 )\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_249&sess=sess162
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