Byung-Ki Kwon
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I am a Ph.D. student at POSTECH, South Korea, advised by Prof. Tae-Hyun Oh.
My research focuses on generative models for images, videos, and multi-modal data (e.g., RGB–depth), with a particular emphasis on diffusion-based architectures. I am interested in enhancing user-perceived quality and practical efficiency of generative systems by incorporating geometry- and motion-aware modeling.
During my M.S. studies at POSTECH, I worked on motion analysis and depth estimation, which provided a strong foundation in modeling scene structure and dynamics.
Keywords: generative model / motion estimation / depth estimation / but not limited to
Contact: byungki.kwon[at]postech[dot]ac[dot]kr
Information: CV, Google Scholar, Linkedin
Work Experience
Research Internship, SonyAI
Apr. 2026 – Current
Research Internship, Naver Cloud
Oct. 2025 – Apr. 2026
Research Internship, Microsoft Research Asia
Sep. 2024 – Mar. 2025
Education
Ph.D. of Artificial Intelligence (advisor: Tae-Hyun Oh)
Master of Electrical Engineering (advisor: Tae-Hyun Oh)
Bachelor of Mechanical Engineering
POSTECH, Mar. 2023
POSTECH, Sep. 2020 – Feb. 2023
POSTECH, Mar. 2013 – Aug 2019
Publications
Early Failure Detection and Intervention in Video Diffusion Models, arXiv 2026
Early Failure Detection and Intervention in Video Diffusion Models, arXiv 2026
Kwon Byung-Ki, Sohwi Lim, Nam Hyeon-Woo, Moon Ye-Bin,
Tae-Hyun Oh
Kwon Byung-Ki, Sohwi Lim, Nam Hyeon-Woo, Moon Ye-Bin,
Tae-Hyun Oh
HDR-NSFF: High Dynamic Range Neural Scene Flow Fields, ICLR 2026
HDR-NSFF: High Dynamic Range Neural Scene Flow Fields, ICLR 2026
Shin Dong-Yeon, Kim Jun-Seong, Kwon Byung-Ki, Tae-Hyun Oh
Shin Dong-Yeon, Kim Jun-Seong, Kwon Byung-Ki, Tae-Hyun Oh
HDR-NSFF.mp4JointDiT: Enhancing RGB-Depth Joint Modeling with Diffusion Transformers, ICCV 2025
*Work performed while interning at Microsoft Research Asia (MSRA)
JointDiT: Enhancing RGB-Depth Joint Modeling with Diffusion Transformers, ICCV 2025
*Work performed while interning at Microsoft Research Asia (MSRA)
Kwon Byung-Ki, Qi Dai, Lee Hyoseok, Chong Luo, Tae-Hyun Oh
Kwon Byung-Ki, Qi Dai, Lee Hyoseok, Chong Luo, Tae-Hyun Oh
jointdit.mp4Zero-shot Depth Completion via Test-time Alignment with Affine-invariant Depth Prior, AAAI 2025
Lee Hyoseok, Kyeong Seon Kim, Kwon Byung-Ki, Tae-Hyun Oh
Learning-based Axial Video Motion Magnification, ECCV 2024
Kwon Byung-Ki, Oh Hyun-Bin, Kim Jun-Seong, Ha Hyunwoo, Tae-Hyun Oh
axial_momag.mp4Revisiting Deep Video Motion Magnification for Real-time Applications, Under review
Ha Hyunwoo, Oh Hyun-Bin, Kim Jun-Seong, Kwon Byung-Ki, Kim Sung-Bin, Ji-Yun Kim, Sung-Ho Bae, Tae-Hyun Oh
realtime_mm.mp4Uni-DVPS: Unified Model for Depth-Aware Video Panoptic Segmentation, RA-L 2024
Kim Ji-Yeon, Oh Hyun-Bin, Kwon Byung-Ki, Dahun Kim, Yongjin Kwon, Tae-Hyun Oh
The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning, ACCV 2024
Jo Won-jun, Kwon Byung-Ki, Kim Ji-Yeon, Hawook Jeong, Kyungdon Joo, Tae-Hyun Oh
DFlow: Learning to Synthesize Better Optical Flow datasets via a Differentiable Pipeline, ICLR 2023
Kwon Byung-Ki, Nam Hyeon-Woo, Kim Ji-Yun, Tae-Hyun Oh
The Devil in the Details: Simple and Effective Optical Flow Data Generation, TVCJ 2024
Kwon Byung-Ki, Kim Sung-Bin, Tae-Hyun Oh
Awards
Outstanding Reviewer Award, ICCV 2023
Samsung HumanTech Paper Award, 2023 (5,000$)
Best Paper Award, Asian Federation of Computer VIsion (AFCV), 2024
Best Paper Award, Workshop on Image Processing and Image Understanding (IPIU), 2023
Best Paper Award, Workshop on Image Processing and Image Understanding (IPIU), 2021
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