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DTSTAMP:20260114T163710Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T170000
DTEND;TZID=Australia/Melbourne:20231212T174800
UID:siggraphasia_SIGGRAPH Asia 2023_sess142@linklings.com
SUMMARY:Rendering
DESCRIPTION:FuseSR: Super Resolution for Real-time Rendering through Effic
 ient Multi-resolution Fusion\n\nThe workload of real-time rendering is ste
 eply increasing as the demand for high resolution, high refresh rates, and
  high realism rises, overwhelming most graphics cards. To mitigate this pr
 oblem, one of the most popular solutions is to render images at a low reso
 lution to reduce rendering overhead,...\n\n\nZhihua Zhong (State Key Lab o
 f CAD&CG, Zhejiang University; Zhejiang University City College); Jingsen 
 Zhu (State Key Lab of CAD&CG, Zhejiang University); Yuxin Dai (Zhejiang A&
 F University); Chuankun Zheng (State Key Lab of CAD&CG, Zhejiang Universit
 y); Guanlin Chen (Zhejiang University City College); Yuchi Huo (Zhejiang L
 ab; State Key Lab of CAD&CG, Zhejiang University); and Hujun Bao and Rui W
 ang (State Key Lab of CAD&CG, Zhejiang University)\n---------------------\
 nInput-Dependent Uncorrelated Weighting for Monte Carlo Denoising\n\nImage
 -space denoising techniques have been widely employed in Monte Carlo rende
 ring, typically blending neighboring pixel estimates using a denoising ker
 nel. It is widely recognized that a kernel should be adapted to characteri
 stics of the input pixel estimates in order to ensure robustness to diver.
 ..\n\n\nJonghee Back (Gwangju Institute of Science and Technology), Binh-S
 on Hua (Trinity College Dublin), Toshiya Hachisuka (University of Waterloo
 ), and Bochang Moon (Gwangju Institute of Science and Technology)\n-------
 --------------\nAdaptive Recurrent Frame Prediction with Learnable Motion 
 Vectors\n\nThe utilization of dedicated ray tracing graphics cards has con
 tributed to the production of stunning visual effects in real-time renderi
 ng. However, the demand for high frame rates and high resolutions remains 
 a challenge to be addressed. A crucial technique for increasing frame rate
  and resolution...\n\n\nZhizhen Wu (State Key Lab of CAD&CG, Zhejiang Univ
 ersity); Chenyu Zuo (State Key Lab of CAD&CG, State Key Laboratory of CAD 
 & CG, Zhejiang University); Yuchi Huo (State Key Lab of CAD&CG, Zhejiang U
 niversity; Zhejiang Lab); Yazhen Yuan (Tencent); Yifan Peng (The Universit
 y of Hong Kong (HKU)); Guiyang Pu (China Mobile (Hangzhou) Information Tec
 hnology Co., Ltd); and Rui Wang and Hujun Bao (State Key Lab of CAD&CG, Zh
 ejiang University)\n---------------------\nFast-MSX: Fast Multiple Scatter
 ing Approximation\n\nClassical microfacet theory suffers from energy loss 
 on materials with high roughness due to the single bounce assumption of mo
 st microfacet models. When roughness is high, there is a large chance of m
 ultiple scattering occurring among the microfacets of the surface. Without
  explicitly modelling for...\n\n\nEnrique Rosales (Huawei) and Fatemeh Tei
 mury, Joshua Horacsek, Aria Salari, Xuebin Qin, Adi Bar-Lev, Xiaoqiang Zhe
 , and Ligang Liu (Huawei Technologies)\n---------------------\nMonte Carlo
  Denoising via Multi-scale Auxiliary Feature Fusion Guided Transformer\n\n
 We present an adversarial transformer-based Monte Carlo denoising network 
 guided by multi-scale auxiliary feature. Extensive experiments demonstrate
  the method's superiority in quantitative metrics and visual perception co
 mpared with state-of-the-art methods.\n\n\nBingyi Chen and Zengyu Liu (Cen
 ter for Future Media, University of Electronic Science and Technology of C
 hina; School of Computer Science and Engineering, University of Electronic
  Science and Technology of China); Li Yuan (Institute of Military Politica
 l Work, Academy of Military Sciences); Zhitao Liu and Yi Li (Center for Fu
 ture Media, University of Electronic Science and Technology of China; Scho
 ol of Computer Science and Engineering, University of Electronic Science a
 nd Technology of China); Guan Wang (Yangtze Delta Region Institute of Univ
 ersity of Electronic Science and Technology of China); and Ning Xie (Cente
 r for Future Media, University of Electronic Science and Technology of Chi
 na; School of Computer Science and Engineering, UESCT)\n\nRegistration Cat
 egory: Full Access\n\nSession Chair: Michael Gharbi (Reve AI, Massachusett
 s Institute of Technology (MIT))
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