Motion-Aware Temporal Confidence Capping in ReSTIR
Evan Luo
Undergraduate Student in Computer Science
University of California Santa Barbara · Department of Computer Science
I am an undergraduate student at the Department of Computer Science, University of California Santa Barbara. My research interests include Real-time Rendering, Differentiable Rendering, and Geometry Processing.
My current research focuses on adaptive variance control for ReSTIR-based path reuse algorithms, building on the GRIS framework to derive closed-form optimal confidence caps from a bias-variance tradeoff analysis. I am also interested in geometry processing pipelines and have contributed to Blender as an open-source developer.
Recent updates and announcements
Will join Huawei as a research intern working on super-resolution.
Started B.Sc. in Computer Science at UC Santa Barbara.
Complete list of publications and preprints by year
Evan Luo
Academic background
University of California Santa Barbara
2025 - 2029 (Expected)
GPA: 4.0/4.0
Research and industry positions
Huawei
Hong Kong Research Center
Incoming research intern focusing on super-resolution.
Beijing Freedo Technology
Research Center
Develop Differentiable Physics Simulation & Differentiable Rendering pipeline for Video Generation of Fluid Dynamics with Taichi (e.g. FluidNexus-like pipeline).
University of California Santa Barbara
Four Eyes Lab
Working with Prof. Tobias Höllerer and Will Zhang on Open-vocabulary Semantic Segmentation.
Open-source tools and research software
A real-time rendering framework in C++ using Slang RHI and Slang shaders, featuring a device-host consistent Scene API inspired by Falcor, a modular RenderGraph system supporting composable RenderPasses, and a cross-language interop layer using HostFxr that enables RenderPasses to be authored in C# while the core runtime remains in C++.
A reproduction of ReSTIR BDPT in Falcor built solely from the paper without access to original source code, implementing bidirectional path tracing with resampled importance sampling.
Contributed to the Grease Pencil module, implementing debugging layer/group traversal and UI integration across C++ core and Python RNA APIs, and investigated and fixed issues related to Grease Pencil tree structures including node ordering, parent-child relationships, and iteration correctness.
Feel free to reach out for collaborations or inquiries