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!
Research Interest
Physically Based Rendering, Neural Rendering
News
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Nov 2024 - I will be participating at SIGGRAPH ASIA 2024! See you guys there!!
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Oct 2024 - I’ve been acknowledged by Krafton’s recent neural rendering paper which aims to accelerate the optimization using semi gradients!
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Mar 2024 - Our work has been accepeted to CVPR 2024!!
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Aug 2023 - I’m pursuing Ph.D. in our SGVR Lab!
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Jul 2023 - I got a Best Master's Thesis Award from Korea Computer Graphics Society! Thank you for the honor!
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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!
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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.
Education
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PhD, Computer Science, KAIST (Aug 2023 ~ Present)
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GPA 4.22/4.3
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MS, Computer Sciene, KAIST (Aug 2021 ~ Aug 2023)
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GPA 4.2/4.3
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BS, Computer Science, Minor in Electrical Engineering, KAIST (Mar 2017 ~ Aug 2021)
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Cum Laude (GPA 3.80/4.3)
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Korea Science Academy of KAIST (Feb 2014 ~ Feb 2017)
Publications
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UFORecon: Generalizable Sparse-View Surface Reconstruction from Arbitrary and UnFavOrable SEts
CVPR 2024
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User-Controlled Layout Editing with Neural Style Transfer
Proceedings of Korea Computer Grahpics Society Conference (KCGS) 2023
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Monte Carlo Image Denoising using Spatial Information of Light Bounces
Proceedings of Korea Information Science Society Conference (KCC) 2022
Invited Talks & Writings
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Pixel-wise Guidance for Utilizing Auxiliary Features in Monte Carlo Denoising
KCGS 2023
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Computer Graphics: Creating a New World Inside the Computer
KAIST magazine “Behind Science” 2023
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Accelerating Deep-Learning-Based Denoising Methods via High Performance Computing
GPU tutorial of KSC 2022
Experience
Scholarships and Awards
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Best Master's Thesis Award from Korea Computer Graphics Society (2023)
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Excellence Award as Teaching Assistant in School of Computing, KAIST (2022 Fall, 2022 Spring)
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Excellence Award in Undergraduate Research Program in KAIST (2021)
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Korea Presidential Science Scholarship ($10K per year) (2017~2020)
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SK Hynix Scholarship ($10K per year, guaranteed employment) (2019~)
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Korea National Representative of APAO (Asia-Pacific Astronomy Olympiad) (2013, 2015)
Won a Silver Medal in APAO 2013 Junior session
Teaching
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Teaching Assistant, Computer Graphics (CS500), Fall 2023
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Teaching Assistant, Data Structure (CS206), Spring 2023, Fall 2022, Spring 2022
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Teaching Assistant, Interactive Computer Graphics (CS482), Fall 2021
Projects
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Kernel Refinement for Monte Carlo Denoising using Pixel-wise Discriminator (2022)
Class Project (CS580)
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Voted as Best Project
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Refine denoising kernel based on pixel-wise scores of the U-Net discriminator
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Indoor Spatial Modeling and Rendering via Neural Rendering (2021)
Research Project
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Contribution of Auxiliary Features to Monte Carlo Denoisers Based on Deep Learning (2021)
Research Project (Excellence Award on Undergraduate Research Program in 2021)
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Analyzing & Enhancing contributions of auxiliary features (albedo, normal, and depth) by adding channel attention to existing denoisers
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Applied multi-task learning by adding auxiliary tasks of reconstructing auxiliary features from a denoised image to enhance the semantics of auxiliary features
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Scene Generator with Blender 2.93 (2021)
Research Project
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Generates scenes with various camera, material, and lighting settings by path-tracing using Cycles renderer supported by Blender
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Extracts geometric features (albedo, normal, depth) of each scene
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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.
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Simple Ray Tracer on CPU (2020)
Individual Project
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Re-implemented the ray tracer following the book series Ray Tracing in One Weekend
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Motion Detector for Interactive Online Real-time Class (2020)
Class Project (Introduction to AI CS470, A+)
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Detects simple hand gestures and alarms sudden movement for interactive online classes
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Contributing to preprocessing data and implementing an application using Openpose and Zoom SDK
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Mini-C Interpreter (2019)
Class Project (Compiler Design CS420)
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Implemented a mini-C interpreter with an error handler that handles common syntax errors
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Contributed to constructing syntax rules of mini-C and implementing lexer and parser
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MADMOVIE, a movie-critics website (2019)
Individual Project
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Implemented and managed server and database based on Node.js and PostgreSQL
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Crawled information and critics of currently screening movies using Beautiful Soup and Python
