  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.

Graphics;Machine Learning