The most recent efforts in video matting have focused on eliminating trimap
dependency since trimap annotations are expensive and trimap-based methods are
less adaptable for real-time applications. Despite the latest tripmap-free
methods showing promising results, their performance often degrades when
dealing with highly diverse and unstructured videos. We address this limitation
by introducing Adaptive Matting for Dynamic Videos, termed AdaM, which is a
framework designed for simultaneously differentiating foregrounds from
backgrounds and capturing alpha matte details of human subjects in the
foreground. Two interconnected network designs are employed to achieve this
goal: (1) an encoder-decoder network that produces alpha mattes and
intermediate masks which are used to guide the transformer in adaptively
decoding foregrounds and backgrounds, and (2) a transformer network in which
long- and short-term attention combine to retain spatial and temporal contexts,
facilitating the decoding of foreground details. We benchmark and study our
methods on recently introduced datasets, showing that our model notably
improves matting realism and temporal coherence in complex real-world videos
and achieves new best-in-class generalizability. Further details and examples
are available at this https URL

通过使用编码解码网络和 transformer 网络，本文介绍了一个名为 AdaM 的自适应合成框架，用于动态视频中的前景和背景分割及 alpha 通道遮罩；在多数据集测试中，AdaM 显示出较高的适应性和合成实况，同时取得新的最佳通用性。

动态视频的自适应人像抠图

Adaptive Human Matting for Dynamic Videos

Image matting is a fundamental and challenging problem in computer vision and
graphics. Most existing matting methods leverage a user-supplied trimap as an
auxiliary input to produce good alpha matte. However, obtaining high-quality
trimap itself is arduous, thus restricting the application of these methods.
Recently, some trimap-free methods have emerged, however, the matting quality
is still far behind the trimap-based methods. The main reason is that, without
the trimap guidance in some cases, the target network is ambiguous about which
is the foreground target. In fact, choosing the foreground is a subjective
procedure and depends on the user's intention. To this end, this paper proposes
an improved deep image matting framework which is trimap-free and only needs
several user click interactions to eliminate the ambiguity. Moreover, we
introduce a new uncertainty estimation module that can predict which parts need
polishing and a following local refinement module. Based on the computation
budget, users can choose how many local parts to improve with the uncertainty
guidance. Quantitative and qualitative results show that our method performs
better than existing trimap-free methods and comparably to state-of-the-art
trimap-based methods with minimal user effort.

本文提出了一种基于深度学习的图像抠图方法，不需要传统的 trimap，只需少量用户交互即可消除模糊性，同时引入了不确定性估计模块和本地细化模块，能够精确地预测需要进一步调整的区域，取得了与传统抠图方法相当的效果。