  Real-time optical Motion Capture (MoCap) systems have not benefited from the
advances in modern data-driven modeling. In this work we apply machine learning
to solve noisy unstructured marker estimates in real-time and deliver robust
marker-based MoCap even when using sparse affordable sensors. To achieve this
we focus on a number of challenges related to model training, namely the
sourcing of training data and their long-tailed distribution. Leveraging
representation learning we design a technique for imbalanced regression that
requires no additional data or labels and improves the performance of our model
in rare and challenging poses. By relying on a unified representation, we show
that training such a model is not bound to high-end MoCap training data
acquisition, and exploit the advances in marker-less MoCap to acquire the
necessary data. Finally, we take a step towards richer and affordable MoCap by
adapting a body model-based inverse kinematics solution to account for
measurement and inference uncertainty, further improving performance and
robustness. Project page: https://moverseai.github.io/noise-tail
