  In this paper, we propose the Matting Anything Model (MAM), an efficient and
versatile framework for estimating the alpha matte of any instance in an image
with flexible and interactive visual or linguistic user prompt guidance. MAM
offers several significant advantages over previous specialized image matting
networks: (i) MAM is capable of dealing with various types of image matting,
including semantic, instance, and referring image matting with only a single
model; (ii) MAM leverages the feature maps from the Segment Anything Model
(SAM) and adopts a lightweight Mask-to-Matte (M2M) module to predict the alpha
matte through iterative refinement, which has only 2.7 million trainable
parameters. (iii) By incorporating SAM, MAM simplifies the user intervention
required for the interactive use of image matting from the trimap to the box,
point, or text prompt. We evaluate the performance of MAM on various image
matting benchmarks, and the experimental results demonstrate that MAM achieves
comparable performance to the state-of-the-art specialized image matting models
under different metrics on each benchmark. Overall, MAM shows superior
generalization ability and can effectively handle various image matting tasks
with fewer parameters, making it a practical solution for unified image
matting. Our code and models are open-sourced at
https://github.com/SHI-Labs/Matting-Anything.
