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DTSTAMP:20240214T070245Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T145500
DTEND;TZID=Australia/Melbourne:20231213T150500
UID:siggraphasia_SIGGRAPH Asia 2023_sess144_papers_384@linklings.com
SUMMARY:PSDR-Room: Single Photo to Scene using Differentiable Rendering
DESCRIPTION:Technical Papers\n\nKai Yan (University of California, Irvine;
  Adobe Research); Fujun Luan, Miloš Hašan, Thibault Groueix, and Valentin 
 Deschaintre (Adobe Research); and Shuang Zhao (University of California, I
 rvine)\n\nA 3D digital scene composes many components: lights, materials a
 nd geometries, interacting to reach the desired appearance. Staging such a
  scene is time-consuming and requires both artistic and technical skills. 
 In this work, we propose a system allowing to optimize lighting as well as
  the pose and material of individual objects to match a target image of a 
 room scene, with minimal user input.\n    To this end, we leverage a recen
 t path-space differentiable rendering approach that provides unbiased grad
 ients of the rendering with respect to geometry, lighting, and procedural 
 materials, allowing us to optimize all of these components using gradient 
 descent to visually match the input photo appearance.\n    We use recent s
 ingle-image scene understanding methods to initialize the optimization and
  search for appropriate 3D models and materials. We evaluate our method on
  real photographs of indoor scenes and demonstrate the editability of the 
 resulting scene components.\n\nRegistration Category: Full Access\n\nSessi
 on Chair: Oded Stein (University of Southern California, MIT)
URL:https://asia.siggraph.org/2023/full-program?id=papers_384&sess=sess144
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