Hello, I'm Jiwon Kang

I am an Integrated M.S./Ph.D. student at KAIST AI (Kim Jaechul Graduate School of AI), advised by Seungryong Kim (CVLAB). My research mainly focuses on generative models and their applications (image generation, guidance, sampling), 3D Vision (novel view synthesis, NeRF, Gaussian Splatting), and Unified Multimodal Models.

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News

Experience

Kakao, Seoul
Research Intern
  • Worked on Text-to-Speech research, repurposing Whisper features as a continuous tokenizer for TTS
Mar – May 2026

Publications

* denotes equal contribution.  ·  † denotes corresponding author.

2026

Transferability Between Understanding and Generation in Unified Multimodal Models

Transferability Between Understanding and Generation in Unified Multimodal Models

Jiwon Kang*, Heeji Yoon*, Jaewoo Jung, Jaewon Min, Minkyeong Jeon, Biyeon Hwang, Sangwon Jung, Seungryong Kim†
ECCV
  • Investigated how understanding and generation capabilities (e.g., counting) transfer to one another within unified multimodal models.
  • Showed that transferability depends on the model architecture, and proposed a practical post-training strategy for unified multimodal models that leverages transferability.
Project Page PDF arXiv Code
APPLE: Attribute-Preserving Pseudo-Labeling for Diffusion-Based Face Swapping

APPLE: Attribute-Preserving Pseudo-Labeling for Diffusion-Based Face Swapping

Jiwon Kang*, Yeji Choi*, JoungBin Lee, Wooseok Jang, Jinhyeok Choi, Taekeun Kang, Yongjae Park, Myungin Kim, Seungryong Kim†
CVPR
  • Prior diffusion-based face swapping methods focused on changing facial identity, failing to preserve facial attributes.
  • Proposed a novel pseudo-labeling framework that preserves facial attributes while swapping identities, achieving Pareto-optimal results.
Project Page PDF arXiv Code
A Noise is Worth Diffusion Guidance

A Noise is Worth Diffusion Guidance

Donghoon Ahn*, Jiwon Kang*, Sanghyun Lee, Jaewon Min, Wooseok Jang, Minjae Kim, Hyungwon Cho, Sayak Paul, SeonHwa Kim, Eunju Cha†, Kyong Hwan Jin†, Seungryong Kim†
ICLR
  • Showed that classifier-free guidance can be replaced by refining the initial noise.
  • Enabled high-quality generation without a guidance pass, highlighting the importance of the initial noise in diffusion models.
Project Page PDF arXiv Code

2025

Where and How to Perturb: On the Design of Perturbation Guidance in Diffusion and Flow Models

Where and How to Perturb: On the Design of Perturbation Guidance in Diffusion and Flow Models

Donghoon Ahn*, Jiwon Kang*, Sanghyun Lee, Minjae Kim, Wooseok Jang, Jaewon Min, Sangwu Lee, Sayak Paul, Seungryong Kim†
NeurIPS
  • Systematically studied perturbation guidance in diffusion and flow models.
  • Showed that perturbing different layers/heads in a diffusion model exhibits different effects on the generated samples, which can be systematically combined to improve user-specified attributes via the proposed framework.
Project Page PDF arXiv Code
Self-Evolving Neural Radiance Fields

Self-Evolving Neural Radiance Fields

Jaewoo Jung*, Jisang Han*, Jiwon Kang*, Seongchan Kim, Min-Seop Kwak, Seungryong Kim†
ICCVW
  • Proposed a self-training framework that lets a NeRF improve itself from few input views via pseudo-label distillation.
Project Page PDF arXiv Code

Preprints & Under Review

RAIN-GS: Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting

RAIN-GS: Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting

Jaewoo Jung*, Jisang Han*, Honggyu An*, Jiwon Kang*, Seonghoon Park*, Seungryong Kim†
arXiv 350+ Stars
  • Relaxed the accurate point-cloud initialization requirement of 3D Gaussian Splatting, enabling training from sparse or random initialization.
Project Page PDF arXiv Code