Occlusion-aware benchmarking in 3D human pose and shape estimation

3D human pose and shape reconstruction is a widely studied area in computer vision. In addition to non-rigid features and highly articulated joints, another challenge in this area is occlusion, which is common in nature. Although some methods explicitly try to handle occlusion cases, the benchmark against which they are evaluated is vague. The typical approach in the literature is to report the performance of the method on an occlusion-oriented subset. However, to form such a subset, it is necessary to quantify the occlusion in the samples. The existing approach uses the keypoints and bounding boxes to quantify and rank samples based on occlusion. However, it fails in several cases and tends to produce false positives. This study proposes the Occlusion Index, a novel index to quantify occlusion in images with high accuracy. The instance mask-based approach not only successfully quantifies occlusion, but also discriminates between occluders and occluders. It also reports the self-occlusion of a person, which is an unavoidable phenomenon in single-view reconstruction. The experiments show the superiority of the Occlusion Index by forming more challenging subsets, causing state-of-the-art occlusion-robust methods to fail more often. Also, some of the most occluded samples in the popular 3D human pose and shape estimation datasets are included.