  Natural image matting algorithms aim to predict the transparency map
(alpha-matte) with the trimap guidance. However, the production of trimap often
requires significant labor, which limits the widespread application of matting
algorithms on a large scale. To address the issue, we propose Matte Anything
(MatAny), an interactive natural image matting model that could produce
high-quality alpha-matte with various simple hints. The key insight of MatAny
is to generate pseudo trimap automatically with contour and transparency
prediction. In our work, we leverage vision foundation models to enhance the
performance of natural image matting. Specifically, we use the segment anything
model to predict high-quality contour with user interaction and an
open-vocabulary detector to predict the transparency of any object.
Subsequently, a pre-trained image matting model generates alpha mattes with
pseudo trimaps. MatAny is the interactive matting algorithm with the most
supported interaction methods and the best performance to date. It consists of
orthogonal vision models without any additional training. We evaluate the
performance of MatAny against several current image matting algorithms. MatAny
has 58.3% improvement on MSE and 40.6% improvement on SAD compared to the
previous image matting methods with simple guidance, achieving new
state-of-the-art (SOTA) performance. The source codes and pre-trained models
are available at https://github.com/hustvl/Matte-Anything.
