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- W4367000234 abstract "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." @default.
- W4367000234 created "2023-04-27" @default.
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- W4367000234 date "2023-04-24" @default.
- W4367000234 modified "2023-09-27" @default.
- W4367000234 title "Occlusion Robust 3D Human Pose Estimation with StridedPoseGraphFormer and Data Augmentation" @default.
- W4367000234 doi "https://doi.org/10.48550/arxiv.2304.12069" @default.
- W4367000234 hasPublicationYear "2023" @default.
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