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. During my Ph.D., I was a research intern at Disney Research Studios in Zurich. My research focuses on improving the conditional generative process by controlling the diffusion dynamics and designing efficient architectures. In parallel, I investigate how temporal correlations can be exploited to enhance generative modeling and inverse problem solving.
My recent research spans three key areas, including but not limited to:
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Generative Models: Diffusion models for video inverse problems, video frame interpolation, and image generation/editing.
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Efficient Generative Modeling: Designing efficient architectures and training/sampling frameworks for diffusion models.
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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
Experience
Disney Research
Research Internship @ Zürich
Mentor: Vinicius Azevedo
2025.05 - 2025.08
ETH Zürich
Visiting Researcher
Joint research program with Disney Research
2025.05 - 2025.08
Research
Convolutional Diffusion Models
Taesung Kwon, L. Bianchi, L. Wittke, F. Watine, F. Carrara, J. C. Ye, R. Weber, V. C. Azevedo
To appear
Fully convolutional diffusion model.
Zero4D: Training-Free 4D Video Generation From Single Video Using Off-the-Shelf Video Diffusion Model
https://zero4dvid.github.io/
J. Park, Taesung Kwon, J. C. Ye
arXiv, 2025
Zero-shot 4D video generation using off-the-shelf video diffusion model.
VISION-XL: High Definition Video Inverse Problem Solver using Latent Diffusion Models
https://vision-xl.github.io/
Taesung Kwon, J. C. Ye
ICCV 2025
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.
Video Diffusion Posterior Sampling for Seeing Beyond Dynamic Scattering Layers
https://github.com/star-kwon/VDPS
Taesung Kwon*, G. Song*, Y. Kim, J. Kim, J. C. Ye, M. Jang (*co-first)
IEEE 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
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, Granted, No. 12,346,997, 2025
Korean Patent, Granted, No. 10-2643601, 2024
- Method and Apparatus for Generating Intermediate Video Frames via Bidirectional Sampling
J. C. Ye, S. Yang, Taesung Kwon
Korean Patent, Filed, No. 10-2025-0114505, 2025
- Method and Apparatus for Solving Video Inverse Problem using Image Diffusion Models
J. C. Ye, Taesung Kwon
Korean Patent, Filed, No. 10-2025-0171861, 2025
Awards and Honors
- KAIST Scholarship, KAIST, 2022-2025
- Korean Government Scholarship, KAIST, 2020-2021
Media
- TPAMI Research featured on YTN News (a major Korean news channel), 2025 youtube
Services
- Conference reviewers: CVPR, ICLR, ICCV, ECCV, ICML
- Journal reviewers: IEEE Transactions on Computational Imaging
- Lab Manager, BISPL, 2024