Kyubeom Han

I am a Ph.D. student in computer science highly interested in physically based rendering. Specifically, my research aims to accelerate physically based rendering methods via deep learning. Not limited to this, I m also interested in recent rendering techniques such as implicit neural representations and differentiable rendering, which I did some projects about these areas. I’m looking forward to the day when people can enjoy realistic rendering anytime and anywhere!
qbhan@kaist.ac.kr, smarthkb98@gmail.com, qbhan98@gmail.com
Google Scholar

Research Interest

Ray Tracing, Physically Based Rendering, Neural Rendering


Mar 2024 - Our work has been accepeted to CVPR 2024!!
Aug 2023 - I’m pursuing Ph.D. in our SGVR Lab!
Jul 2023 - I got a Best Master's Thesis Award from Korea Computer Graphics Society! Thank you for the honor!
May 2023 - I just presented our work in I3D 2023, which is held in Unity Technology, Seattle! It was amazing to interact with graphics researchers worldwide!
Sep 2022 - I gave a talk at the Korean domestic conference, KSC 2022. This talk is about optimizing neural networks and training schemes on a large GPU machine to accelerate training and testing.


PhD, Computer Science, KAIST (Aug 2023 ~ Present)
GPA 4.22/4.3
MS, Computer Sciene, KAIST (Aug 2021 ~ Aug 2023)
GPA 4.2/4.3
BS, Computer Science, Minor in Electrical Engineering, KAIST (Mar 2017 ~ Aug 2021)
Cum Laude (GPA 3.80/4.3)
Korea Science Academy of KAIST (Feb 2014 ~ Feb 2017)


UFORecon: Generalizable Sparse-View Surface Reconstruction from Arbitrary and UnFavOrable SEts
Youngju Na, Woo Jae Kim, Kyu Beom Han, Suhyeon Ha, Sung-eui Yoon
Kyu Beom Han, Olivia G. Odenthal, Woo Jae Kim, Sung-Eui Yoon
User-Controlled Layout Editing with Neural Style Transfer
Yaxin Wang, Kyu Beom Han, Jaeyoon Kim, Sung-Eui Yoon
Proceedings of Korea Computer Grahpics Society Conference (KCGS) 2023
Monte Carlo Image Denoising using Spatial Information of Light Bounces
Kyu-Beom Han, Sung-Eui Yoon
Proceedings of Korea Information Science Society Conference (KCC) 2022

Invited Talks & Writings

Pixel-wise Guidance for Utilizing Auxiliary Features in Monte Carlo Denoising
KCGS 2023
Computer Graphics: Creating a New World Inside the Computer
KAIST magazine “Behind Science” 2023
Accelerating Deep-Learning-Based Denoising Methods via High Performance Computing
GPU tutorial of KSC 2022


Research Intern @SGVR lab, KAIST (Dec 2020 ~ Jun 2021)
Undergraduate Research Intern @SGVR lab, KAIST (Jun 2020 ~ Jun 2021)
Data Engineer Intern @Humelo (Dec 2019 ~ Feb 2020)
Quality Assurance Intern @SK Hynix (Dec 2018 ~ Feb 2019)
Undergraduate Research Intern @ACE lab, KAIST (Jun 2018 ~ Aug 2018)

Scholarships and Awards

Best Master's Thesis Award from Korea Computer Graphics Society (2023)
Excellence Award as Teaching Assistant in School of Computing, KAIST (2022 Fall, 2022 Spring)
Excellence Award in Undergraduate Research Program in KAIST (2021)
Korea Presidential Science Scholarship ($10K per year) (2017~2020)
SK Hynix Scholarship ($10K per year, guaranteed employment) (2019~)
Korea National Representative of APAO (Asia-Pacific Astronomy Olympiad) (2013, 2015)
Won a Silver Medal in APAO 2013 Junior session


Teaching Assistant, Computer Graphics (CS500), Fall 2023
Teaching Assistant, Data Structure (CS206), Spring 2023, Fall 2022, Spring 2022
Teaching Assistant, Interactive Computer Graphics (CS482), Fall 2021


Kernel Refinement for Monte Carlo Denoising using Pixel-wise Discriminator (2022)
Class Project (CS580)
Voted as Best Project
Refine denoising kernel based on pixel-wise scores of the U-Net discriminator
Indoor Spatial Modeling and Rendering via Neural Rendering (2021)
Research Project
Contribution of Auxiliary Features to Monte Carlo Denoisers Based on Deep Learning (2021)
Research Project (Excellence Award on Undergraduate Research Program in 2021)
Analyzing & Enhancing contributions of auxiliary features (albedo, normal, and depth) by adding channel attention to existing denoisers
Applied multi-task learning by adding auxiliary tasks of reconstructing auxiliary features from a denoised image to enhance the semantics of auxiliary features
Scene Generator with Blender 2.93 (2021)
Research Project
Generates scenes with various camera, material, and lighting settings by path-tracing using Cycles renderer supported by Blender
Extracts geometric features (albedo, normal, depth) of each scene
Current Monte-Carlo denoising methods involves deep learning which requires a lot of data. This scene generator is expected to make rich training dataset for this task easily.
Simple Ray Tracer on CPU (2020)
Individual Project
Re-implemented the ray tracer following the book series Ray Tracing in One Weekend
Motion Detector for Interactive Online Real-time Class (2020)
Class Project (Introduction to AI CS470, A+)
Detects simple hand gestures and alarms sudden movement for interactive online classes
Contributing to preprocessing data and implementing an application using Openpose and Zoom SDK
Question Answering on KORQUAD Dataset (2020)
Class Project (Deep Learning for Real-World Problems CS492i, A+)
Achieved 2nd place out of 8 competing teams with test accuracy of 70.4%
Contributed to improving prediction among multiple contexts
Product Classification on NAVER Produce Dataset (2020)
Class Project (Deep Learning for Real-World Problems CS492i, A+)
Achieved 3rd place out of 10 competing teams with given data with only 2% labeled data
Contributed to implementing hybrid learning method of MixMatch and SimCLR
Mini-C Interpreter (2019)
Class Project (Compiler Design CS420)
Implemented a mini-C interpreter with an error handler that handles common syntax errors
Contributed to constructing syntax rules of mini-C and implementing lexer and parser
MADMOVIE, a movie-critics website (2019)
Individual Project
Implemented and managed server and database based on Node.js and PostgreSQL
Crawled information and critics of currently screening movies using Beautiful Soup and Python