Songyin Wu

Songyin Wu

University of California, Santa Barbara

Short Bio

My name is Songyin Wu (吴松隐). I am a Ph.D. student at the University of California, Santa Barbara, advised by Prof. Ling-Qi Yan. My research mainly focuses on rendering. I’m especially interested in exploring novel representations for efficient real-time rendering.

Interests

  • Rendering
  • Inverse Graphics

Education

  • BSc in Computer Science, 2017 ~ 2021

    Turing Class, Peking University

  • Ph.D. Student in Computer Science, 2022 ~ now

    University of California, Santa Barbara

Recent Publications

GFFE: G-buffer Free Frame Extrapolation for Low-latency Real-time Rendering

Real-time rendering has been embracing ever-demanding effects, such as ray tracing. However, rendering such effects in high resolution …
Songyin Wu, Deepak Vembar, Anton Sochenov, Selvakumar Panneer, Sungye Kim, Anton Kaplanyan, Ling-Qi Yan
Paper Video

Unified Gaussian Primitives for Scene Representation and Rendering

Searching for a unified scene representation remains a research challenge in computer graphics. Traditional mesh-based representations …
Yang Zhou, Songyin Wu, Ling-Qi Yan
Website Paper

ExtraSS: A Framework for Joint Spatial Super Sampling and Frame Extrapolation

We introduce ExtraSS, a novel framework that combines spatial super sampling and frame extrapolation to enhance real-time rendering …
Songyin Wu, Sungye Kim, Zheng Zeng, Deepak Vembar, Sangeeta Jha, Anton Kaplanyan, Ling-Qi Yan
Website Paper Supplemental Video

BSDF Importance Baking: A Lightweight Neural Solution to Importance Sampling General Parametric BSDFs

Parametric Bidirectional Scattering Distribution Functions (BSDFs) are pervasively used because of their flexibility to represent a …
Yaoyi Bai, Songyin Wu, Zheng Zeng, Beibei Wang, Ling-Qi Yan
Paper

Projects

Songyin Wu, Lingqi Yan
Sep 1, 2021

Microfacet Material Energy Compensation

We proposed a neural network approach for microfacet material energy compensation. Our method only takes roughness and F0 parameters for GGX model and predicts energy compensated BRDF values. The model is very effective in the inference stage, and can handle isotropic/anisotropic, colored materials.

Experience

 
 
 
 
 

Research Scientist Intern

Meta

Jun 2024 – Jan 2025 Washington, USA
 
 
 
 
 

Research Scientist Intern

Intel Corporation

Jun 2023 – Sep 2023 California, USA
 
 
 
 
 

Research Scientist Intern

Microsoft Research Asia

Dec 2021 – Jul 2022 Beijing, China