Demo Data from SIGIR 2026 / reviewers in the wild / expert
Sangeeta Jha
dblp:362/9541
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0001-7301-6009ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Rendering · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Rendering › temporal rendering
frame extrapolation |
0.7 | 1 | 2023 | ExtraSS: A Framework for Joint Spatial Super Sampling and Frame Extrapolation · SIGGRAPH Asia 2023 |
Rendering
real-time rendering |
0.7 | 1 | 2023 | ExtraSS: A Framework for Joint Spatial Super Sampling and Frame Extrapolation · SIGGRAPH Asia 2023 |
Methods — techniques the papers use, named apart from their topics
warping · 0.7neural network · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | ExtraSS: A Framework for Joint Spatial Super Sampling and Frame ExtrapolationabstractWe introduce ExtraSS, a novel framework that combines spatial super sampling and frame extrapolation to enhance real-time rendering performance. By integrating these techniques, our approach achieves a balance between 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, improve rendering sharpness, handle moving shadings accurately, and generate temporally stable results. Computational costs are significantly reduced compared to traditional rendering methods, enabling higher frame rates and alias-free high resolution results. Evaluation using Unreal Engine demonstrates the benefits of our framework over conventional individual spatial or temporal super sampling methods, delivering improved rendering speed and visual 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. Songyin Wu, Sungye Kim, Zheng Zeng 0005, Deepak Vembar, Sangeeta Jha, Anton Kaplanyan, Lingqi Yan 0001 |
SIGGRAPH Asia | 5 |