  This paper performs comprehensive analysis on datasets for occlusion-aware
face segmentation, a task that is crucial for many downstream applications. The
collection and annotation of such datasets are time-consuming and
labor-intensive. Although some efforts have been made in synthetic data
generation, the naturalistic aspect of data remains less explored. In our
study, we propose two occlusion generation techniques, Naturalistic Occlusion
Generation (NatOcc), for producing high-quality naturalistic synthetic occluded
faces; and Random Occlusion Generation (RandOcc), a more general synthetic
occluded data generation method. We empirically show the effectiveness and
robustness of both methods, even for unseen occlusions. To facilitate model
evaluation, we present two high-resolution real-world occluded face datasets
with fine-grained annotations, RealOcc and RealOcc-Wild, featuring both careful
alignment preprocessing and an in-the-wild setting for robustness test. We
further conduct a comprehensive analysis on a newly introduced segmentation
benchmark, offering insights for future exploration.
