  Recent image matting studies are developing towards proposing trimap-free or
interactive methods for complete complex image matting tasks. Although avoiding
the extensive labors of trimap annotation, existing methods still suffer from
two limitations: (1) For the single image with multiple objects, it is
essential to provide extra interaction information to help determining the
matting target; (2) For transparent objects, the accurate regression of alpha
matte from RGB image is much more difficult compared with the opaque ones. In
this work, we propose a Unified Interactive image Matting method, named UIM,
which solves the limitations and achieves satisfying matting results for any
scenario. Specifically, UIM leverages multiple types of user interaction to
avoid the ambiguity of multiple matting targets, and we compare the pros and
cons of different annotation types in detail. To unify the matting performance
for transparent and opaque objects, we decouple image matting into two stages,
i.e., foreground segmentation and transparency prediction. Moreover, we design
a multi-scale attentive fusion module to alleviate the vagueness in the
boundary region. Experimental results demonstrate that UIM achieves
state-of-the-art performance on the Composition-1K test set and a synthetic
unified dataset.
