  This paper introduces an innovative approach for image matting that redefines
the traditional regression-based task as a generative modeling challenge. Our
method harnesses the capabilities of latent diffusion models, enriched with
extensive pre-trained knowledge, to regularize the matting process. We present
novel architectural innovations that empower our model to produce mattes with
superior resolution and detail. The proposed method is versatile and can
perform both guidance-free and guidance-based image matting, accommodating a
variety of additional cues. Our comprehensive evaluation across three benchmark
datasets demonstrates the superior performance of our approach, both
quantitatively and qualitatively. The results not only reflect our method's
robust effectiveness but also highlight its ability to generate visually
compelling mattes that approach photorealistic quality. The project page for
this paper is available at
https://lightchaserx.github.io/matting-by-generation/
