Bio

I am currently a PhD student at Center for Visual Computing of UC San Diego, advised by professor Ravi Ramamoorthi. Here my focus and interest are mostly on rendering related problems. I am also gratefully receiving countless guidance from professor Tzumao Li and Dr. Iliyan Georgiev. Previously, I obtained my bachelor degree from University of Hong Kong (CS) and was working a bit on knowledge-based question answering system.

I have had the fortune to intern at Adobe Graphics Research and Meta Reality Labs for the past couple of summers, working with amazing mentors and colleagues.

Before PhD, I worked as a rendering research engineer at ZJU-Manycore Tech joint Lab of CG&AI, led by Dr. Rui Tang and Prof. Rui Wang, where I mainly worked on our self-developed Render Engine FF (aka fast and furious :P), Monte Carlo sampling, denoising and other graphics related topics. I also spent a short time at Alibaba and Sensetime working on stream processing and face recognition, respectively.

Research

I am mainly interested in physically based rendering, light transport and learning-based approaches for sampling, appearance modelling etc. And more broadly, computer graphics and inverse rendering problems.

BTW Reserving this spot for my favoriate paper yet to come.. hopefully very soon. This is one screenshot from a RPG game we made years ago.

Importance Sampling BRDF Derivatives
Yash Belhe, Bing Xu, Sai Praveen Bangaru, Ravi Ramamoorthi, Tzu-Mao Li
ACM Transactions on Graphics, 2024
project page

Yash's great work on importance sampling real-valued derivates, enabling better recovery of spatially varying textures through GD-based inverse rendering.

Neusample: Importance sampling for neural materials
Bing Xu, Liwen Wu, Miloš Hašan, Fujun Luan, Iliyan Georgiev, Zexiang Xu, Ravi Ramamoorthi
ACM SIGGRAPH, 2023
project page / paper / code

A simple toolbox composed of three approaches to efficiently importance sample neural SVBRDFs, potentially facilitating the broader adoption of neural material models in production.

Adversarial Monte Carlo Denoising with Conditioned Auxiliary Feature Modulation
Bing Xu, Junfei Zhang, Rui Wang, Kun Xu, Yong-liang Yang, Chuan Li, Rui Tang
ACM Transactions on Graphics (TOG), 38(6), ACM SIGGRAPH Asia, 2019
project page / paper / code

Denoising Monte Carlo renderings by better utilizing the first bounce auxiliary features and adversarial loss.

Gallery

if it can be called a gallery.... I do some drawing and painting when I'm doing well or struggling with research. I might stop doing research once my paintings start making money - you know, the best-case scenario, after the painter's death.

In construction.. coming in June. This website doesn't work on mobile phones either now. To be fixed..

Miscellany

• Zombies