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Research Interest

I have mainly researched generative models with multi-modal data for vairous applications with different conditions.
Generative Models. e.g., Diffusion Models, Image-to-Image Translation, Style Transfer etc.
Multimodal Learning. e.g., Audio-guided Image Manipulation, Text-guided Image Manipulation, etc.

Meanwhile, I am mainly interested in instructive, interactive, and personalized practical applications by understanding the intention of user actions. Additionally, I am also interested in Generative models & understanding and learning relations or representations in images and between images and other modalities, visual perception(detection, detection), and their further extensions to the 3D or video applications.

Education

  • Korea University, Seoul, Korea

    • M.S. Student in Computer Science and Engineering
    • Mar. 2021 - Aug. 2023
  • Dongguk University, Seoul, Korea

    • B.S. in Computer Engineering
    • Mar. 2014 - Aug. 2020

Experience

  • Naver AI Research Intern (Naver, Seongnam, Korea)

    • June. 2022 - Nov. 2022
    • Mentor: Gayoung Lee
  • Undergraduate Intern (Korea University CVLAB, Seoul, Korea)

    • Aug. 2020 - Feb. 2021
    • Advisor: Prof. Seungryong Kim

Publications

International Journal

Controllable Style Transfer via Test-time Training of Implicit Neural Representation

Sunwoo Kim*, Youngjo Min, Younghoon Jeong and Seungryong Kim
ArXiv Preprint, 2022.
[Project Page] [arXiv]

International Conference

DiffMatch: Diffusion Model for Dense Matching

Jisu Nam, Gyuseong Lee, Sunwoo Kim, Hyunsu Kim, Hyungwon Cho, Seyeon Kim, Seungryong Kim
International Conference on Learning Representations (ICLR, 1.2% Oral presentation), 2024.
[arXiv]

User-friendly Image Editing with Minimal Text Input: Leveraging Captioning & Injection Technique

Sunwoo Kim*, Wooseok Jang, Hyunsu Kim, Junho Kim, Yunjei Cho, Seungryong Kim and Gayoung Lee
[arXiv]

LANIT: LAnguage-Driven Unsupervised Image-to-Image Translation for Unlabeled Data

Jihye Park*, Sunwoo Kim*, Soohyun Kim*, Seokju Cho, Jaejun Yoo, Youngjung Uh and Seungryong Kim
Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[Project Page] [arXiv]

Deep Translation Prior: Test-time Training for Photorealistic Style Transfer

Sunwoo Kim*, Soohyun Kim* and Seungryong Kim
36th AAAI Conference on Artificial Intelligence, (AAAI) 2022.
[Github] [arXiv]

Projects

  • Human talking face Generation Projects
    • June. 2022 - Nov. 2022

SunwooKim's Projects

awesome-diffusion-models icon awesome-diffusion-models

A collection of resources and papers on Diffusion Models and Score-based Models, a darkhorse in the field of Generative Models

colorization icon colorization

Automatic colorization using deep neural networks. "Colorful Image Colorization." In ECCV, 2016.

crossattentioncontrol icon crossattentioncontrol

Unofficial implementation of "Prompt-to-Prompt Image Editing with Cross Attention Control" with Stable Diffusion

crossattentioncontrol-stablediffusion icon crossattentioncontrol-stablediffusion

Unofficial implementation of "Prompt-to-Prompt Image Editing with Cross Attention Control" with Stable Diffusion, the code is based on the offical StableDiffusion repository.

deep_translation_prior icon deep_translation_prior

Official repository for Deep Translation Prior: Test-time Training for Photorealistic Style Transfer (AAAI 2022)

diffusionclip icon diffusionclip

[CVPR 2022] Official PyTorch Implementation for DiffusionCLIP: Text-guided Image Manipulation Using Diffusion Models

etri_segmentation_dpt icon etri_segmentation_dpt

Semantic Segmentation for Art Domain with Semi-Supervised Learning with Transformer Backbone.

inr-st icon inr-st

Official repository for Controllable Style Transfer via Test-time Training of Implicit Neural Representation

lanit icon lanit

Official repository for LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data

liif icon liif

Learning Continuous Image Representation with Local Implicit Image Function, in CVPR 2021 (Oral)

moco icon moco

PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722

text2live icon text2live

Official Pytorch Implementation for "Text2LIVE: Text-Driven Layered Image and Video Editing" (ECCV 2022 Oral)

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