Video portrait segmentation (VPS), aiming at segmenting prominent foreground portraits from video frames, has received much attention in recent years. However, simplicity of existing VPS datasets leads to a limitation on extensive research of the task. In this work, we propose a new intricate large-scale Multi-scene Video Portrait Segmentation dataset MVPS consisting of 101 video clips in 7 scenario categories, in which 10,843 sampled frames are finely annotated at pixel level. The dataset has diverse scenes and complicated background environments, which is the most complex dataset in VPS to our best knowledge. Through the observation of a large number of videos with portraits during dataset construction, we find that due to the joint structure of human body, motion of portraits is part-associated, which leads that different parts are relatively independent in motion. That is, motion of different parts of the portraits is unbalanced. Towards this unbalance, an intuitive and reasonable idea is that different motion states in portraits can be better exploited by decoupling the portraits into parts. To achieve this, we propose a Part-Decoupling Network (PDNet) for video portrait segmentation. Specifically, an Inter-frame Part-Discriminated Attention (IPDA) module is proposed which unsupervisely segments portrait into parts and utilizes different attentiveness on discriminative features specified to each different part. In this way, appropriate attention can be imposed to portrait parts with unbalanced motion to extract part-discriminated correlations, so that the portraits can be segmented more accurately. Experimental results demonstrate that our method achieves leading performance with the comparison to state-of-the-art methods.

提出了一种新的复杂大规模多场景视频人像分割数据集（MVPS），通过对10,843个样本帧进行像素级精细标注，以实现对视频帧中突出的前景人像进行分割。引入了一种Part-Decoupling Network（PDNet）的方法，包括Inter-frame Part-Discriminated Attention（IPDA）模块，该模块能够无监督地将人像分割为不同的部分，并针对每个不同的部分指定不同的注意力来提取部分间的关联性，从而实现更准确的人像分割。实验结果表明，该方法在同类方法中具有领先的性能。

面向不平衡动作的视频肖像分割的部分解耦网络