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BEGIN:VEVENT
DTSTAMP:20240214T070311Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T152000
DTEND;TZID=Australia/Melbourne:20231213T162500
UID:siggraphasia_SIGGRAPH Asia 2023_sess125@linklings.com
SUMMARY:Light, Shadows & Curves
DESCRIPTION:Technical Papers, TOG\n\nAn Adaptive Fast-Multipole-Accelerate
 d Hybrid Boundary Integral Equation Method for Accurate Diffusion Curves\n
 \nIn theory, diffusion curves promise complex color gradations for infinit
 e-resolution vector graphics. In practice, existing realizations suffer fr
 om poor scaling, discretization artifacts, or insufficient support for ric
 h boundary conditions. Previous applications of the boundary element metho
 d to d...\n\n\nSeungbae Bang (University of Toronto, Amazon); Kirill Serkh
  (University of Toronto); Oded Stein (University of Southern California, M
 IT); and Alec Jacobson (University of Toronto, Adobe)\n-------------------
 --\nSOL-NeRF: Sunlight Modeling for Outdoor Scene Decomposition and Religh
 ting\n\nOutdoor scenes often involve large-scale geometry and complex unkn
 own lighting conditions, making it difficult to decompose them into geomet
 ry, reflectance and illumination. Recently researchers made attempts to de
 compose outdoor scenes using Neural Radiance Fields (NeRF) and learning-ba
 sed lighting...\n\n\nJia-Mu Sun and Tong Wu (Institute of Computing Techno
 logy, Chinese Academy of Sciences; University of Chinese Academy of Scienc
 es); Yong-Liang Yang (University of Bath); Yu-Kun Lai (Cardiff University)
 ; and Lin Gao (Institute of Computing Technology, Chinese Academy of Scien
 ces; University of Chinese Academy of Sciences)\n---------------------\nSp
 atiotemporally Consistent HDR Indoor Lighting Estimation\n\nWe propose a p
 hysically-motivated deep learning framework to solve a general version of 
 the challenging indoor lighting estimation problem. Given a single LDR ima
 ge with a depth map, our method predicts spatially consistent lighting at 
 any given image position. Particularly, when the input is an LDR...\n\n\nZ
 hengqin Li (Meta, University of California San Diego); Yu Li and Mikhail O
 kunev (Meta); Manmohan Chandraker (University of California San Diego); an
 d Zhao Dong (Meta)\n---------------------\nShadow Harmonization for Realis
 tic Compositing\n\nCompositing virtual objects into real background images
  requires one to carefully match the scene's camera parameters, surface ge
 ometry, textures, and lighting to obtain plausible renderings.\nRecent lea
 rning approaches have shown many scene properties can be estimated from im
 ages, resulting in robus...\n\n\nLucas Valença and Jinsong Zhang (Universi
 té Laval), Michaël Gharbi and Yannick Hold-Geoffroy (Adobe), and Jean-Fran
 çois Lalonde (Université Laval)\n---------------------\nReShader: View-Dep
 endent Highlights for Single Image View-Synthesis\n\nIn recent years, nove
 l view synthesis from a single image has seen significant progress thanks 
 to the rapid advancements in 3D scene representation and image inpainting 
 techniques. While the current approaches are able to synthesize geometrica
 lly consistent novel views, they often do not handle the ...\n\n\nAvinash 
 Paliwal and Brandon G. Nguyen (Texas A&M University), Andrii Tsarov (Leia 
 Inc.), and Nima Khademi Kalantari (Texas A&M University)\n\nRegistration C
 ategory: Full Access\n\nSession Chair: Michael Gharbi (Adobe, MIT)
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