Shin seong Kim

2 accepted papers

2025

Balanced Conic Rectified Flow

Shin seong Kim, Mingi Kwon, Jaeseok Jeong, Youngjung Uh

NeurIPS 2025poster

Rectified flow is a generative model that learns smooth transport mappings between two distributions through an ordinary differential equation (ODE). The model learns a straight ODE by reflow steps which iteratively update the supervisory flow. It allows for a relatively simple and efficient generat…

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2025

TCFG: Tangential Damping Classifier-free Guidance

Mingi Kwon, Shin seong Kim, Jaeseok Jeong, Yi Ting Hsiao, Youngjung Uh

CVPR 2025poster

Diffusion models have achieved remarkable success in text-to-image synthesis, largely attributed to the use of classifier-free guidance (CFG), which enables high-quality, condition-aligned image generation. CFG combines the conditional score (e.g., text-conditioned) with the unconditional score to c…

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