  Most previous image matting methods require a roughly-specificed trimap as
input, and estimate fractional alpha values for all pixels that are in the
unknown region of the trimap. In this paper, we argue that directly estimating
the alpha matte from a coarse trimap is a major limitation of previous methods,
as this practice tries to address two difficult and inherently different
problems at the same time: identifying true blending pixels inside the trimap
region, and estimate accurate alpha values for them. We propose AdaMatting, a
new end-to-end matting framework that disentangles this problem into two
sub-tasks: trimap adaptation and alpha estimation. Trimap adaptation is a
pixel-wise classification problem that infers the global structure of the input
image by identifying definite foreground, background, and semi-transparent
image regions. Alpha estimation is a regression problem that calculates the
opacity value of each blended pixel. Our method separately handles these two
sub-tasks within a single deep convolutional neural network (CNN). Extensive
experiments show that AdaMatting has additional structure awareness and trimap
fault-tolerance. Our method achieves the state-of-the-art performance on Adobe
Composition-1k dataset both qualitatively and quantitatively. It is also the
current best-performing method on the alphamatting.com online evaluation for
all commonly-used metrics.
