Predicting Camera Viewpoint Improves Cross-dataset Generalization for 3D Human Pose Estimation
Predicting Camera Viewpoint Improves Cross-dataset Generalization for 3D Human Pose Estimation
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DOI：
10.1007/978-3-030-66096-3_36
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Zhe Wang;Daeyun Shin;Charless C. Fowlkes
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文献类型：
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作者：
Zhe Wang;Daeyun Shin;Charless C. Fowlkes
随着大型地面真实运动捕捉数据集的出现，3D 人体姿势的单目估计引起了越来越多的关注。然而，可用训练数据的多样性是有限的，并且尚不清楚方法在其训练的特定数据集之外的泛化程度如何。在这项工作中，我们对特定数据集中存在的多样性和偏差及其对 5 个姿势数据集的跨数据集泛化的影响进行了系统研究。我们特别关注相机视点相对于以身体为中心的坐标系分布的系统差异。基于这一观察，我们提出了除了姿势之外预测相机视点的辅助任务。我们发现，经过训练以系统地联合预测视点和姿势的模型显示出显着改善的跨数据集泛化能力。
Monocular estimation of 3d human pose has attracted increased attention with the availability of large ground-truth motion capture datasets. However, the diversity of training data available is limited and it is not clear to what extent methods generalize outside the specific datasets they are trained on. In this work we carry out a systematic study of the diversity and biases present in specific datasets and its effect on cross-dataset generalization across a compendium of 5 pose datasets. We specifically focus on systematic differences in the distribution of camera viewpoints relative to a body-centered coordinate frame. Based on this observation, we propose an auxiliary task of predicting the camera viewpoint in addition to pose. We find that models trained to jointly predict viewpoint and pose systematically show significantly improved cross-dataset generalization.