About
I'm a Staff Engineer at Samsung Electronics, working in the Neural Graphics Group within the System LSI division. I work in real-time neural graphics, with a focus on frame generation. Before this, my research was about using machine learning to solve computer vision problems, especially those related to motion and video. I've stayed on that path, now pushing this technology forward in real-time graphics applications.
Research interests: graphics, video, optical flow, NeRF
Award
- Naver Ph.D Fellowship Award, 2022.
- Finalist at Qualcomm Innovation Fellowship Korea (QIFK), 2020.
- Outstanding Teaching Assistant Award (์ฐ์์กฐ๊ต์), KAIST, 2019.
Education
- KAIST, Ph.D., Computer Science2018-2024
- Advisor: Professor Sung-Eui Yoon
- KAIST, M.S., Computer Science2016-2018
- Advisor: Professor Hyun Seung Yang
- Yonsei University, B.S., Computer Science2012-2016
Work
- System LSI, Samsung Electronics2025.06-current
- Samsung Advanced Institute of Technology (SAIT)Samsung Electronics2024.08-2025.06
- CLOVA, NAVER Cloud Corp. (internship)2023.02-2023.08
Service
- SGVR KAIST Website (Skill: web frontend, Wordpress, PHP)2018-2024
- GPU Cluster (Skill: Docker, Kubernetes, Grafana)2023-2024
Publication
-
Regularizing Dynamic Radiance Fields with Kinematic Fields
European Conference on Computer Vision (ECCV)
[To appear]
- Fine-grained Background Representation for Weakly Supervised Semantic Segmentation IEEE Transactions on Circuits and Systems for Video Technology (TCSVT)
- Extending Segment Anything Model into Auditory and Temporal Dimensions for Audio-Visual Segmentation International Conference on Image Processing (ICIP)
- SemCity: Semantic Scene Generation with Triplane Diffusion Conference on Computer Vision and Pattern Recognition (CVPR)
-
Multi-resolution distillation for self-supervised monocular depth estimation
Pattern Recognition Letters
- Diffusion Probabilistic Models for Scene-Scale 3D Categorical Data Workshop on Image Processing and Image Understanding (IPIU)
- Scenario Generation by Action Scene-Graph Prediction Korea Software Congress (KSC)
- Semi-Supervised Learning of Optical Flow by Flow Supervisor European Conference on Computer Vision (ECCV)
- In-N-Out: Towards Good Initialization for Inpainting and Outpainting British Machine Vision Conference (BMVC)
- Self-Supervised Visual Odometry via Frame Interpolation Korea Robotics Society Annual Conference (KRoC)
- Unsupervised Learning of Optical Flow with Deep Feature Similarity European Conference on Computer Vision (ECCV)
- Combined Center Dispersion Loss Function for Deep Facial Expression Recognition Pattern Recognition Letters
- Two-stream Spatiotemporal Feature for Video QA Task https://arxiv.org/abs/1907.05006
- Acoustic Material Estimation with Convolutional Neural Network Korea Robotics Society Annual Conference (KRoC)
- An Application of Convolutional-LSTM Network and Video QA Korea Computer Congress (KCC)
- Scale-Varying Triplet Ranking with Classification Loss for Facial Age Estimation Asian Conference on Computer Vision (ACCV)
- CBVMR: Content-Based Video-Music Retrieval Using Soft Intra-Modal Structure Constraint Proceedings of the ACM international conference on Multimedia Retrieval (ICMR)
- D3: Recognizing dynamic scenes with deep dual descriptor based on key frames and key segments Neurocomputing
-
SSPP-DAN: Deep Domain Adaptation Network for Face Recognition with Single Sample Per Person
International Conference on Image Processing (ICIP'17), IEEE
Oral [paper]
- Convolutional Texture Networks based on Histogram Pooling
- Image-text multi-modal representation learning by adversarial backpropagation arXiv preprint arXiv:1612.08354
- Deep CNN-based Person Identification using Facial and Clothing Features Jun 2016, Summer General Conference '16, IEEK
Patent
- ํธ๋ฆฌํ๋ฆฟ ๊ธฐ๋ฐ์ ์์คํจ์๋ฅผ ํ์ฉํ ์์๊ฐ ์๋ ๋ถ๋ฅ๋ฌธ์ ๋ฅผ ์ํ ๋ฅ๋ฌ๋ ๋ชจ๋ธ ํ์ต ๋ฐฉ๋ฒ ๋ฐ ์ฅ์น (Using Triplet-based Loss for Training Ordinal Classification Deep Models)
[US App] [KR App] - ๋ถ๋ถ ์ด๋ฏธ์ง ๊ธฐ๋ฐ ๊ฐ์ฒด ํ๋ณ ๋ฐฉ๋ฒ ๋ฐ ์ฅ์น (Partial Face Based Person Identification Across Poses)
[KR] - ๋๋ฉ์ธ ์ ์ ๊ธฐ๋ฐ ๊ฐ์ฒด ์ธ์ ๋ชจ๋ธ ์ ๊ณต ์ฅ์น ๋ฐ ๋ฐฉ๋ฒ (APPARATUS AND METHOD FOR PROVIDING OBJECT RECOGNITION MODEL BASED ON DOMAIN ADAPTATION)
[KR App] - ๊ดํ ํ๋ฆ ์ถ์ ์ ์ํ ๋ฅ ์ ์ฌ๋ ๊ธฐ๋ฐ ๋น์ง๋ ํ์ต์ ์ปดํจํฐ ์์คํ
๋ฐ ๊ทธ์ ๋ฐฉ๋ฒ (COMPUTER SYSTEM OF UNSUPERVISED LEARNING WITH DEEP SIMILARITY FOR OPTICAL FLOW ESTIMATION AND METHOD THEREOF)
[KR] [US App]
Experience
- Teaching Assistants
- GSAG-KAIST Research and Education Program, 1/2019-2/2019
- CS206: Data Structure (Spring 2019), 3/2019-6/2019
- CS688: Web-Scale Image Retrieval (Fall 2018), 9/2018-12/2018
- CS101: Introduction to Programming, 9/2016-12/2017, 9/2018-12/2018
- Undergraduate Research Assistant
- DB Lab, Yonsei University, 1/2014-2/2014