Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2025

Abstract

In this paper, we introduce DiffusionMat, a novel image matting framework that employs a diffusion model for the transition from coarse to refined alpha mattes. Diverging from conventional methods that utilize trimaps merely as loose guidance for alpha matte prediction, our approach treats image matting as a deterministic sequential refinement learning process. This process begins with the addition of noise to trimaps and iteratively denoises them using a pre-trained diffusion model, which incrementally guides the prediction towards a clean alpha matte. The key innovation of our framework is a correction module that adjusts the output at each denoising step, ensuring that the final result is consistent with the input image's structures. We also introduce the Alpha Reliability Propagation, a novel technique designed to maximize the utility of available guidance by selectively enhancing the trimap regions with confident alpha information, thus simplifying the correction task. To train the correction module, we devise specialized loss functions that target the accuracy of the alpha matte's edges and the consistency of its opaque and transparent regions. We evaluate our model across several image matting benchmarks, and the results indicate that DiffusionMat consistently outperforms existing methods.

Keywords

Image Matting, Diffusion Models, Sequential Learning

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Areas of Excellence

Digital transformation

Publication

MM '25: Proceedings of the 33rd ACM International Conference on Multimedia, Dublin, Ireland, October 27-31

First Page

9454

Last Page

9462

Identifier

10.1145/3746027.3754903

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3746027.3754903

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