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 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. Project page at~\url{this https URL
Abstract (translated)
在本文中，我们引入了DiffusionMat，一种新颖的图像遮罩框架，它采用扩散模型来从粗粒度到精细粒度的alpha遮罩的转变。与仅仅利用trimap作为粗略指导的常规方法不同，我们的方法将图像遮罩处理为序列细化学习过程。这个过程从对trimap添加噪声开始，并使用预训练的扩散模型逐步去噪，从而逐步引导预测朝向干净的alpha遮罩。我们框架的关键创新是具有修正模块，它在每个去噪步骤中对输出进行调整，确保最终结果与输入图像的结构保持一致。我们还引入了Alpha可靠性传播，一种旨在通过选择性地增强自信的alpha信息来最大程度地利用可用的指导的技术，从而简化修正任务。为了训练修正模块，我们设计了一些针对alpha遮罩边缘准确性和一致性的专门损失函数。我们在多个图像遮罩基准测试中评估我们的模型，结果表明DiffusionMat consistently优于现有方法。项目页面链接为：https://this URL
URL
https://arxiv.org/abs/2311.13535
