  Image matting requires high-quality pixel-level human annotations to support
the training of a deep model in recent literature. Whereas such annotation is
costly and hard to scale, significantly holding back the development of the
research. In this work, we make the first attempt towards addressing this
problem, by proposing a self-supervised pre-training approach that can leverage
infinite numbers of data to boost the matting performance. The pre-training
task is designed in a similar manner as image matting, where random trimap and
alpha matte are generated to achieve an image disentanglement objective. The
pre-trained model is then used as an initialisation of the downstream matting
task for fine-tuning. Extensive experimental evaluations show that the proposed
approach outperforms both the state-of-the-art matting methods and other
alternative self-supervised initialisation approaches by a large margin. We
also show the robustness of the proposed approach over different backbone
architectures. Our project page is available at
https://crystraldo.github.io/dpt_mat/.
