This paper introduces a new matting task called human instance matting (HIM),
which requires the pertinent model to automatically predict a precise alpha
matte for each human instance. Straightforward combination of closely related
techniques, namely, instance segmentation, soft segmentation and
human/conventional matting, will easily fail in complex cases requiring
disentangling mingled colors belonging to multiple instances along hairy and
thin boundary structures. To tackle these technical challenges, we propose a
human instance matting framework, called InstMatt, where a novel mutual
guidance strategy working in tandem with a multi-instance refinement module is
used, for delineating multi-instance relationship among humans with complex and
overlapping boundaries if present. A new instance matting metric called
instance matting quality (IMQ) is proposed, which addresses the absence of a
unified and fair means of evaluation emphasizing both instance recognition and
matting quality. Finally, we construct a HIM benchmark for evaluation, which
comprises of both synthetic and natural benchmark images. In addition to
thorough experimental results on complex cases with multiple and overlapping
human instances each has intricate boundaries, preliminary results are
presented on general instance matting. Code and benchmark are available in
this https URL

提出了一种称为人物实例抠图（HIM）的新型抠图任务，旨在自动预测每个人物实例的精确 alpha 抠图。通过引入名为 InstMatt 的人物实例抠图框架，并使用新颖的互相指导策略以及多实例细化模块应对复杂的情况，本文提高了抠图的质量和准确性。我们还提出了一种新的实例抠图质量度量标准（IMQ），并构建了 HIM 基准测试集，包括合成和自然图像。