Pith. sign in

REVIEW 5 cited by

FlowDPS: Flow-Driven Posterior Sampling for Inverse Problems

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2503.08136 v1 pith:P55233CX submitted 2025-03-11 cs.CV cs.AIcs.LG

Jeongsol Kim , Bryan Sangwoo Kim , Jong Chul Ye This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords flowinversediffusionposteriorsamplingflowdpsmodelsbeen
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Flow matching is a recent state-of-the-art framework for generative modeling based on ordinary differential equations (ODEs). While closely related to diffusion models, it provides a more general perspective on generative modeling. Although inverse problem solving has been extensively explored using diffusion models, it has not been rigorously examined within the broader context of flow models. Therefore, here we extend the diffusion inverse solvers (DIS) - which perform posterior sampling by combining a denoising diffusion prior with an likelihood gradient - into the flow framework. Specifically, by driving the flow-version of Tweedie's formula, we decompose the flow ODE into two components: one for clean image estimation and the other for noise estimation. By integrating the likelihood gradient and stochastic noise into each component, respectively, we demonstrate that posterior sampling for inverse problem solving can be effectively achieved using flows. Our proposed solver, Flow-Driven Posterior Sampling (FlowDPS), can also be seamlessly integrated into a latent flow model with a transformer architecture. Across four linear inverse problems, we confirm that FlowDPS outperforms state-of-the-art alternatives, all without requiring additional training.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PASDiff: Physics-Aware Semantic Guidance for Joint Real-World Low-Light Face Enhancement and Restoration

    cs.CV 2026-03 conditional novelty 6.0 of 10

    PASDiff steers an unconditional diffusion model with inverse-intensity exposure maps, Retinex reflectance anchors, and AdaIN-aligned structural priors to restore identity-consistent faces from compound low-light degra...

  2. Solving Inverse Problems with Flow-based Models via Model Predictive Control

    eess.IV 2026-01 conditional novelty 6.0 of 10

    MPC-Flow applies model predictive control to guide pretrained flow models through inverse problems, with a single-step variant that avoids backpropagation and scales to 32B-parameter models on consumer hardware.

  3. InverseCrafter: Efficient Video ReCapture as a Latent Domain Inverse Problem

    cs.CV 2025-12 conditional novelty 6.0 of 10

    A training-free, near-zero-overhead inverse solver for novel-view video generation and inpainting that projects masks into continuous multi-channel latent masks and applies DDS with conjugate gradient in latent space.

  4. DICT: Data Injection and Contrastive Trajectory Refinement for Conditional Image Generation with Diffusion Models

    cs.CV 2026-07 conditional novelty 5.5 of 10

    Noise-perturbed condition injection plus contrastive trajectory refinement improves training-free conditional diffusion sampling across style transfer, super-resolution and deblurring.

  5. Translationese as a Rational Response to Translation Task Difficulty

    cs.CL 2026-03 unverdicted novelty 5.0 of 10

    Translationese is partly predictable from quantifiable translation-task difficulty, especially cross-lingual transfer load, more so for English-to-German than the reverse.

Pith tools