We present a novel locality-based learning method for cleaning and solving optical motion capture data. Given noisy marker data, we propose a new heterogeneous graph neural network which treats markers and joints as different types of nodes, and uses graph convolution operations to extract the local features of markers and joints and transform them to clean motions. To deal with anomaly markers (e.g. occluded or with big tracking errors), the key insight is that a marker's motion shows strong correlations with the motions of its immediate neighboring markers but less so with other markers, a.k.a. locality, which enables us to efficiently fill missing markers (e.g. due to occlusion). Additionally, we also identify marker outliers due to tracking errors by investigating their acceleration profiles. Finally, we propose a training regime based on representation learning and data augmentation, by training the model on data with masking. The masking schemes aim to mimic the occluded and noisy markers often observed in the real data. Finally, we show that our method achieves high accuracy on multiple metrics across various datasets. Extensive comparison shows our method outperforms state-of-the-art methods in terms of prediction accuracy of occluded marker position error by approximately 20%, which leads to a further error reduction on the reconstructed joint rotations and positions by 30%. The code and data for this paper are available at https://github.com/non-void/LocalMoCap.

我们提出了一种新颖的基于位置的学习方法，用于清理和解决光学动作捕捉数据。我们提出了一种新的异构图神经网络，将标记点和关节视为不同类型的节点，并使用图卷积操作提取标记点和关节的局部特征并将其转化为清晰的动作。我们的方法在多个数据集上取得了高准确性，相对于最先进方法，对于遮挡的标记点位置误差的预测准确性提高了约20％，从而进一步降低了重建关节旋转和位置的误差约30％。

基于区域的神经解算器用于光学动作捕捉