Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Sangeeta Jha

dblp:362/9541 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Rendering › temporal rendering
frame extrapolation
0.712023
ExtraSS: A Framework for Joint Spatial Super Sampling and Frame Extrapolation · SIGGRAPH Asia 2023
Rendering
real-time rendering
0.712023
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
YearPublicationVenuePosition
2023 ExtraSS: A Framework for Joint Spatial Super Sampling and Frame Extrapolation
abstract
We 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 Asia5