Occlusion Robust 3D Human Pose Estimation with StridedPoseGraphFormer and Data Augmentation
This addresses occlusion robustness for 3D human pose estimation, which is an incremental improvement over existing methods.
The paper tackles the problem of occlusion in 3D human pose estimation by proposing a method that combines spatio-temporal features with synthetic occlusion augmentation during training, showing it compares favorably with state-of-the-art methods and that existing methods degrade significantly under occlusion.
Occlusion is an omnipresent challenge in 3D human pose estimation (HPE). In spite of the large amount of research dedicated to 3D HPE, only a limited number of studies address the problem of occlusion explicitly. To fill this gap, we propose to combine exploitation of spatio-temporal features with synthetic occlusion augmentation during training to deal with occlusion. To this end, we build a spatio-temporal 3D HPE model, StridedPoseGraphFormer based on graph convolution and transformers, and train it using occlusion augmentation. Unlike the existing occlusion-aware methods, that are only tested for limited occlusion, we extensively evaluate our method for varying degrees of occlusion. We show that our proposed method compares favorably with the state-of-the-art (SoA). Our experimental results also reveal that in the absence of any occlusion handling mechanism, the performance of SoA 3D HPE methods degrades significantly when they encounter occlusion.