Dogyun Park

M.S & Ph.D Integrated Student in MLV Lab, advised by Prof. Hyunwoo J. Kim.

Department of Computer Science and Engineering at Korea University, Seoul, Republic of Korea.

My research interests are Generative AI in computer vision, especially in making efficient and effective diffusion models/rectified flows. My ultimate goal is to push the boundaries of generative AI to enable more creative, efficient, and controllable solutions for real-world applications, from content creation to scientific simulations.

I am actively seeking opportunities to contribute to impactful projects in the field of Generative AI. If you are interested in collaboration or have opportunities that align with my expertise, please feel free to reach out to me via contact email.

news

Nov 10, 2024	I am honored to serve as a reviewer for ICLR 2025.
Sep 29, 2024	Diffusion prior-based amortized variational inference for noisy inverse problems is accepted as Oral at ECCV 2024!
Sep 25, 2024	Constant Acceleration Flow is accepted at NeurIPS 2024!

selected publications [full list]

(*) denotes equal contribution

NeurIPS

Constant Acceleration Flow

Dogyun Park, Sojin Lee, Sihyeon Kim, Taehoon Lee, Youngjoon Hong, and Hyunwoo J Kim

Neural Information Processing Systems, NeurIPS

Bib PDF

@article{park2024constant,
  title = {Constant Acceleration Flow},
  author = {Park, Dogyun and Lee, Sojin and Kim, Sihyeon and Lee, Taehoon and Hong, Youngjoon and Kim, Hyunwoo J},
  journal = {Neural Information Processing Systems, NeurIPS},
  year = {2024},
}

ECCVOral

Diffusion prior-based amortized variational inference for noisy inverse problems

Sojin Lee*, Dogyun Park*, Inho Kong, and Hyunwoo J Kim

European Conference on Computer Vision, ECCV

Oral Presentation [Top 2.3%]

Bib PDF

@article{lee2024diffusion,
  title = {Diffusion prior-based amortized variational inference for noisy inverse problems},
  author = {Lee, Sojin and Park, Dogyun and Kong, Inho and Kim, Hyunwoo J},
  award = {Oral},
  honor = {Oral Presentation [Top 2.3%]},
  journal = {European Conference on Computer Vision, ECCV},
  year = {2024},
}

ICLR

DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations

Dogyun Park, Sihyeon Kim, Sojin Lee, and Hyunwoo J Kim

International Conference on Learning Representations, ICLR

Bib PDF

@article{park2024ddmi,
  title = {DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations},
  author = {Park, Dogyun and Kim, Sihyeon and Lee, Sojin and Kim, Hyunwoo J},
  journal = {International Conference on Learning Representations, ICLR},
  year = {2024},
}

ICCV

Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models

Dogyun Park, and Suhyun Kim

International Conference on Computer Vision, ICCV

Bib PDF

@article{park2023probabilistic,
  title = {Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models},
  author = {Park, Dogyun and Kim, Suhyun},
  journal = {International Conference on Computer Vision, ICCV},
  pages = {20099--20109},
  year = {2023},
}