  Recently, there has been an increasing concern about the privacy issue raised
by using personally identifiable information in machine learning. However,
previous portrait matting methods were all based on identifiable portrait
images. To fill the gap, we present P3M-10k in this paper, which is the first
large-scale anonymized benchmark for Privacy-Preserving Portrait Matting.
P3M-10k consists of 10,000 high-resolution face-blurred portrait images along
with high-quality alpha mattes. We systematically evaluate both trimap-free and
trimap-based matting methods on P3M-10k and find that existing matting methods
show different generalization capabilities when following the
Privacy-Preserving Training (PPT) setting, i.e., "training on face-blurred
images and testing on arbitrary images". To devise a better trimap-free
portrait matting model, we propose P3M-Net, which leverages the power of a
unified framework for both semantic perception and detail matting, and
specifically emphasizes the interaction between them and the encoder to
facilitate the matting process. Extensive experiments on P3M-10k demonstrate
that P3M-Net outperforms the state-of-the-art methods in terms of both
objective metrics and subjective visual quality. Besides, it shows good
generalization capacity under the PPT setting, confirming the value of P3M-10k
for facilitating future research and enabling potential real-world
applications. The source code and dataset will be made publicly available.
