  Video matting aims to predict the alpha mattes for each frame from a given
input video sequence. Recent solutions to video matting have been dominated by
deep convolutional neural networks (CNN) for the past few years, which have
become the de-facto standard for both academia and industry. However, they have
inbuilt inductive bias of locality and do not capture global characteristics of
an image due to the CNN-based architectures. They also lack long-range temporal
modeling considering computational costs when dealing with feature maps of
multiple frames. In this paper, we propose VMFormer: a transformer-based
end-to-end method for video matting. It makes predictions on alpha mattes of
each frame from learnable queries given a video input sequence. Specifically,
it leverages self-attention layers to build global integration of feature
sequences with short-range temporal modeling on successive frames. We further
apply queries to learn global representations through cross-attention in the
transformer decoder with long-range temporal modeling upon all queries. In the
prediction stage, both queries and corresponding feature maps are used to make
the final prediction of alpha matte. Experiments show that VMFormer outperforms
previous CNN-based video matting methods on the composited benchmarks. To our
best knowledge, it is the first end-to-end video matting solution built upon a
full vision transformer with predictions on the learnable queries. The project
is open-sourced at https://chrisjuniorli.github.io/project/VMFormer/
