PP-Matting: High-Accuracy Natural Image Matting
Natural image matting is a fundamental and challenging computer vision task.
It has many applications in image editing and composition. Recently, deep
learning-based approaches have achieved great improvements in image matting.
However, most of them require a user-supplied trimap as an auxiliary input,
which limits the matting applications in the real world. Although some
trimap-free approaches have been proposed, the matting quality is still
unsatisfactory compared to trimap-based ones. Without the trimap guidance, the
matting models suffer from foreground-background ambiguity easily, and also
generate blurry details in the transition area. In this work, we propose
PP-Matting, a trimap-free architecture that can achieve high-accuracy natural
image matting. Our method applies a high-resolution detail branch (HRDB) that
extracts fine-grained details of the foreground with keeping feature resolution
unchanged. Also, we propose a semantic context branch (SCB) that adopts a
semantic segmentation subtask. It prevents the detail prediction from local
ambiguity caused by semantic context missing. In addition, we conduct extensive
experiments on two well-known benchmarks: Composition-1k and Distinctions-646.
The results demonstrate the superiority of PP-Matting over previous methods.
Furthermore, we provide a qualitative evaluation of our method on human matting
which shows its outstanding performance in the practical application. The code
and pre-trained models will be available at PaddleSeg:
this https URL