Publications

Conference Papers

FAST-DIPS: Adjoint-Free Analytic Steps and Hard-Constrained Likelihood Correction for Diffusion-Prior Inverse Problems

Minwoo Kim *, Seunghyeok Shin *, Hongki Lim

Published in International Conference on Learning Representations (ICLR) 2026, 2026

FAST-DIPS is a training-free diffusion-prior inverse-problem solver that enforces a hard measurement-space feasibility constraint via an adjoint-free ADMM correction with an analytic (or forward-difference) step size, plus decoupled re-annealing. It supports linear/nonlinear forward operators without hand-coded adjoints and includes pixel, latent, and hybrid pixel→latent variants for faster, stable reconstructions. OpenReview · PDF · Code

Download Paper

Adaptive 3D Reconstruction via Diffusion Priors and Forward Curvature-Matching Likelihood Updates

Seunghyeok Shin, Dabin Kim, Hongki Lim

Published in The Thirty-Ninth Annual Conference on Neural Information Processing Systems(NeurIPS), Spotlight, 2025

FCM is a training-free, likelihood-guided diffusion update that auto-tunes step sizes via forward-mode autodiff and curvature probes, enabling fast, stable 3D point-cloud reconstruction from single/multi-view inputs. OpenReview · Project page · Code

Download Paper