The content discusses the task of 3D human pose estimation, which involves predicting the spatial positions of human joints from images or videos to reconstruct a 3D skeleton of a human. Recent advancements in deep learning have significantly improved the performance of 3D pose estimation, but traditional methods often fall short as they primarily focus on the spatial coordinates of joints and overlook the orientation and rotation of the connecting bones, which are crucial for a comprehensive understanding of human pose in 3D space.
To address these limitations, the authors introduce Quater-GCN (Q-GCN), a directed graph convolutional network that not only captures the spatial dependencies among node joints through their coordinates but also integrates the dynamic context of bone rotations in 2D space. This approach enables a more sophisticated representation of human poses by also regressing the orientation of each bone in 3D space, moving beyond mere coordinate prediction.
Furthermore, the authors complement their model with a semi-supervised training strategy that leverages unlabeled data, addressing the challenge of limited orientation ground truth data. Through comprehensive evaluations, Q-GCN has demonstrated outstanding performance against current state-of-the-art methods on various datasets, including Human3.6M, HumanEva-I, and H3WB.
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by Xingyu Song,... at arxiv.org 05-01-2024
https://arxiv.org/pdf/2404.19279.pdfDeeper Inquiries