Conventional video matting outputs one alpha matte for all instances
appearing in a video frame so that individual instances are not distinguished.
While video instance segmentation provides time-consistent instance masks,
results are unsatisfactory for matting applications, especially due to applied
binarization. To remedy this deficiency, we propose Video Instance
Matting~(VIM), that is, estimating alpha mattes of each instance at each frame
of a video sequence. To tackle this challenging problem, we present MSG-VIM, a
Mask Sequence Guided Video Instance Matting neural network, as a novel baseline
model for VIM. MSG-VIM leverages a mixture of mask augmentations to make
predictions robust to inaccurate and inconsistent mask guidance. It
incorporates temporal mask and temporal feature guidance to improve the
temporal consistency of alpha matte predictions. Furthermore, we build a new
benchmark for VIM, called VIM50, which comprises 50 video clips with multiple
human instances as foreground objects. To evaluate performances on the VIM
task, we introduce a suitable metric called Video Instance-aware Matting
Quality~(VIMQ). Our proposed model MSG-VIM sets a strong baseline on the VIM50
benchmark and outperforms existing methods by a large margin. The project is
open-sourced at this https URL

视频实例抠图领域研究近期的课题主要集中在视频序列中每个实例的阿尔法抠图的精准性和时间一致性，提出了 Mask Sequence Guided Video Instance Matting（MSG-VIM）作为视频实例抠图的基线模型，通过混合掩膜增强方法和时间特征引导提升了抠图预测的准确性和一致性，新建了包含多个人实例的 VIM50 数据集用于评估模型性能，采用 Video Instance-aware Matting Quality（VIMQ）作为评价指标，MSG-VIM 在 VIM50 数据集上表现出色并大幅超越现有方法。