Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets
This work addresses the problem of labor-intensive dataset creation for face segmentation in computer vision applications, offering incremental improvements in synthetic data generation.
The paper tackles the challenge of creating high-quality synthetic datasets for occlusion-aware face segmentation by proposing two occlusion generation techniques, NatOcc and RandOcc, which are shown to be effective and robust even for unseen occlusions, and introduces two real-world datasets, RealOcc and RealOcc-Wild, for model evaluation.
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.