Wonseok Oh

I am a second-year M.S. student at the University of Michigan. I am currently majoring in Electrical and Computer Engineering (computer vision track).

Currently, I am a research assistant advised by Professor Andrew Owens, focusing on multi-modal learning with sound. I am also collaborating with Professor Qing Qu on diffusion models.

Previously I was a research assistant at the ETRI, Daejeon, South Korea, working with Kimin Yun and Yongju Lee in the summer of 2021 and 2022.
I received a B.S. in Computer Science and Engineering at Korea University and a B.E. in Software Technology and Enterprise Program, Chemical and Biological Engineering in 2022 (triple major!).

My research interests are Computer Vision, Machine Learning, and Robotics. I'm most interested in multimodal learning using sounds and computational creation using generative models.

I am actively seeking PhD positions starting in Fall 2025 :)

Publications


Conference Papers

2024

  1. CVPRW
    From 2D Portraits to 3D Realities: Advancing GAN Inversion for Enhanced Image Synthesis.
    Wonseok Oh*, Youngjoo Jo*
    CVPR 2024 2nd Workshop for Learning 3D with Multi-View Supervision (oral)
    arXiv Paper
  1. CVPRW
    Cross-Domain Generalization: Enhancing Rare Disease Data Representation using Diffusion Model.
    Wonseok Oh
    (under review)
    arXiv Paper

2023

  1. KCC
    Improving quality of pixel-wise transfer using Attention-based Encoding (SAE) and Abortion method.
    Wonseok Oh et al
    Korea Computer Congress 2023 (oral)
    (2nd award in independent study, Korea University)
    arXiv Paper

2021

  1. KSC
    Visualization Comparison of GAN for Reconstructing De-identified Image Dataset using Attention.
    Wonseok Oh, Kangmin Bae, Yuseok Bae
    Korea Software Congress 2021 (KSC 2021) (domestic)
    HTML PAPER


Journal Publications

2021

  1. JOK
    RDID-GAN: Reconstructing a De-identified Image Dataset to Generate Effective Learning Data.
    Wonseok Oh, Kangmin Bae, Yuseok Bae
    Journal of Korean Institute of Information Scientists and Engineers 2021 (JOK 2021) (domestic)
    HTML PAPER