About Me
Hi, my name’s Taesung and I’m a fourth-year Ph.D. candidate in KAIST, advised by Prof. Jong Chul Ye and Prof. Mooseok Jang. My research focuses on computer vision (CV) and its intersection with generative models, exploring their applications in solving inverse problems and enhancing image and video processing. My recent research aims to improve the conditional generative process by controlling the diffusion generative process. Additionally, I explore methods to leverage temporal correlations to improve the performance of inverse problem solvers.
My research spans three key areas:
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Generative Models: Diffusion models for video inverse problems, video frame interpolation, and image editing.
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Low-Level Vision: Unsupervised/self-supervised denoising methods for images and videos.
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Physics-Based Vision and Medical Imaging: Applications to physical sciences (e.g., inverse scattering in optics) and medical imaging.
Education
KAIST
Ph.D. Candidate in Bio and Brain Engineering
Advisors: Jong Chul Ye and Mooseok Jang
2022 - Current
KAIST
M.S. in Bio and Brain Engineering
Advisor: Jong Chul Ye
2020 - 2022
KAIST
B.S. in Bio and Brain Engineering
Advisor: Yoonkey Nam
2015 - 2020
Research
VISION-XL: High Definition Video Inverse Problem Solver using Latent Diffusion Models
https://vision-xl.github.io/
Taesung Kwon, J. C. Ye
arXiv, 2024
Solving HD video inverse problems using only latent diffusion models. Supporting wide-range ratio using SDXL.
ViBiDSampler: Enhancing Video Interpolation Using Bidirectional Diffusion Sampler
https://vibidsampler.github.io/
S. Yang*, Taesung Kwon*, J. C. Ye (*co-first)
ICLR 2025
State-of-the-art performance, efficiently generating high-quality, smooth videos between keyframes.
Taesung Kwon, J. C. Ye
ICLR 2025
Solving video inverse problems using only image diffusion models, with batch-consistent sampling stretagy.
Taesung Kwon*, G. Song*, Y. Kim, J. Kim, J. C. Ye, M. Jang (*co-first)
Submitted to TPAMI
Video reconstruction through dynamic scattering layer using video diffusion models.
Highly Personalized Text Embedding for Image Manipulation by Stable Diffusion
https://hiper0.github.io/
I. Han*, S. Yang*, Taesung Kwon, J. C. Ye
arXiv, 2023
Image manipulation using personalized text embedding from Stable Diffusion.
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation
https://github.com/gwang-kim/DiffusionCLIP
G. Kim, Taesung Kwon, J. C. Ye
CVPR, 2022
Robust text-driven image manipulation using text-to-image diffusion models.
Noise Distribution Adaptive Self-Supervised Image Denoising using Tweedie Distribution and Score Matching
https://github.com/cubeyoung/NoiseAdaptive2Score
K. Kim, Taesung Kwon, J. C. Ye
CVPR, 2022
Noise distribution and level adaptive denoising framework using Tweedie distribution and score matching.
Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoising
https://github.com/star-kwon/TCI_CyclefreeCycleGAN
Taesung Kwon, J. C. Ye
IEEE TCI, 2021
Efficient CycleGAN framework for low-dose CT denoising.
Workshop
NTIRE 2022 Spectral Recovery Challenge and Data Set
CVPRW, 2022
Recovering hyperspectral information from JPEG-compressed RGB images.
NTIRE 2022 Challenge on Night Photography Rendering
CVPRW, 2022
Recovering the visual appearance of night photography.
Patents
- Method and Apparatus for Low-Dose X-Ray Computed Tomography Image Processing Based on Efficient Unsupervised Learning Using Invertible Neural Network
J. C. Ye, Taesung Kwon
U.S. Patent Application, Filed, No. 17/848,689, 2022
Korean Patent, Granted, No. 10-2643601, 2024
Awards and Honors
- KAIST Scholarship, KAIST, 2022-2025
- Korean Government Scholarship, KAIST, 2020-2021
Services
- Conference reviewers: CVPR, ICLR, ECCV, ICML
- Journal reviewers: IEEE Transactions on Computational Imaging
- Lab Manager, BISPL, 2024