Susung Hong

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Welcome to my website! I am a Ph.D. student in GRAIL at the University of Washington, co-advised by Steve Seitz, Brian Curless, and Ira Kemelmacher-Shlizerman. My research interests lie in the foundations of generative AI, and its applications to producing immersive video/3D content.

I love cats, coffee (especially lattes), video games, cooking, and playing baseball. I enjoy learning new things beyond my research area.

If you are interested in collaboration or would like to learn more about my research, please connect:

news

Feb 26, 2025 Our two papers, Perturb-and-Revise and Spatiotemporal Skip Guidance (STG) have been accepted to CVPR 2025.
Jan 15, 2025 I will be joining Adobe Research as a research intern this summer!
Sep 25, 2024 Our two papers, Smoothed Energy Guidance (SEG) and Effective Rank Regularizaiton for 3DGS have been accepted to NeurIPS 2024.

selected publications

  1. Preprint
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    MusicInfuser: Making Video Diffusion Listen and Dance
    Susung Hong, Ira Kemelmacher-Shlizerman, Brian Curless, and Steven M Seitz
    Preprint, 2025
    arXiv Website
  2. CVPR
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    Perturb-and-Revise: Flexible 3D Editing with Generative Trajectories
    Susung Hong, Johanna Karras, Ricardo Martin-Brualla, and Ira Kemelmacher-Shlizerman
    CVPR, 2025
    arXiv Website
  3. CVPR
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    Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling
    Junha Hyung*, Kinam Kim*, Susung Hong, Min-Jung Kim, and Jaegul Choo
    CVPR, 2025
    arXiv Website
  4. NeurIPS
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    Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention
    Susung Hong
    NeurIPS, 2024
    arXiv Website
  5. ICCV
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    Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
    Susung Hong, Gyuseong Lee, Wooseok Jang, and Seungryong Kim
    ICCV, 2023
    arXiv Website