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.
Prior to my PhD studies, I earned my bachelor’s degree at Peking University, where I was fortunate to be supervised by Prof. Baoquan Chen.
Ph.D. Student in Computer Science, 2022 ~ now
University of California, Santa Barbara
BSc in Computer Science, 2017 ~ 2021
Turing Class, Peking University
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.