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DTSTAMP:20240214T070248Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231215T094000
DTEND;TZID=Australia/Melbourne:20231215T095000
UID:siggraphasia_SIGGRAPH Asia 2023_sess155_papers_744@linklings.com
SUMMARY:ExtraSS: A Framework for Joint Spatial Super Sampling and Frame Ex
 trapolation
DESCRIPTION:Technical Papers\n\nSongyin Wu (University of California Santa
  Barbara); Sungye Kim (Intel Corporation); Zheng Zeng (University of Calif
 ornia, Santa Barbara); Deepak Vembar, Sangeeta Jha, and Anton Kaplanyan (I
 ntel Corporation); and Ling-Qi Yan (University of California, Santa Barbar
 a)\n\nWe introduce ExtraSS, a novel framework that combines spatial super 
 sampling and frame extrapolation to enhance real-time rendering performanc
 e. By integrating these techniques, our approach achieves a balance betwee
 n performance and quality, generating temporally stable and high-quality, 
 high-resolution results. Leveraging lightweight modules on warping and the
  ExtraSSNet for refinement, we exploit spatial-temporal information, impro
 ve rendering sharpness, handle moving shadings accurately, and generate te
 mporally stable results. Computational costs are significantly reduced com
 pared to traditional rendering methods, enabling higher frame rates and al
 ias-free high resolution results. Evaluation using Unreal Engine demonstra
 tes the benefits of our framework over conventional individual spatial or 
 temporal super sampling methods, delivering improved rendering speed and v
 isual quality. With its ability to generate temporally stable high-quality
  results, our framework creates new possibilities for real-time rendering 
 applications, advancing the boundaries of performance and photo-realistic 
 rendering in various domains.\n\nRegistration Category: Full Access\n\nSes
 sion Chair: Sing Chun Lee (Bucknell University)
URL:https://asia.siggraph.org/2023/full-program?id=papers_744&sess=sess155
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