My name is Songyin Wu (吴松隐). I am a Ph.D. student at the University of California, Santa Barbara, advised by Prof. Lingqi Yan. My research mainly focuses on rendering. I’m especially interested in exploring novel representations for efficient real-time rendering.
BSc in Computer Science, 2017 ~ 2021
Turing Class, Peking University
Ph.D. Student in Computer Science, 2022 ~ now
University of California, Santa Barbara
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.
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.