Human instance matting aims to estimate an alpha matte for each human
instance in an image, which is extremely challenging and has rarely been
studied so far. Despite some efforts to use instance segmentation to generate a
trimap for each instance and apply trimap-based matting methods, the resulting
alpha mattes are often inaccurate due to inaccurate segmentation. In addition,
this approach is computationally inefficient due to multiple executions of the
matting method. To address these problems, this paper proposes a novel
End-to-End Human Instance Matting (E2E-HIM) framework for simultaneous multiple
instance matting in a more efficient manner. Specifically, a general perception
network first extracts image features and decodes instance contexts into latent
codes. Then, a united guidance network exploits spatial attention and semantics
embedding to generate united semantics guidance, which encodes the locations
and semantic correspondences of all instances. Finally, an instance matting
network decodes the image features and united semantics guidance to predict all
instance-level alpha mattes. In addition, we construct a large-scale human
instance matting dataset (HIM-100K) comprising over 100,000 human images with
instance alpha matte labels. Experiments on HIM-100K demonstrate the proposed
E2E-HIM outperforms the existing methods on human instance matting with 50%
lower errors and 5X faster speed (6 instances in a 640X640 image). Experiments
on the PPM-100, RWP-636, and P3M datasets demonstrate that E2E-HIM also
achieves competitive performance on traditional human matting.

该论文提出了一种新的端到端人体实例分割（E2E-HIM）框架，用于更高效地进行多实例分割。该框架通过使用图像特征提取、空间注意力、语义嵌入等技术，同时生成所有实例级别的 alpha 轮廓图，相较于现有方法在人体实例分割上降低了 50％的错误率并提升了 5 倍的速度。在多个数据集上的实验证明了该方法的竞争性能。

端到端人体实例抠图

End-to-End Human Instance Matting

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。