BundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse
  Multiview Videos
Georgios Albanis Nikolaos Zioulis Kostas Kolomvatsos
Capturing smooth motions from videos using markerless techniques typically
involves complex processes such as temporal constraints, multiple stages with
data-driven regression and optimization, and bundle solving over temporal
windows. These processes can be inefficient and require tuning multiple
objectives across stages. In contrast, BundleMoCap introduces a novel and
efficient approach to this problem. It solves the motion capture task in a
single stage, eliminating the need for temporal smoothness objectives while
still delivering smooth motions. BundleMoCap outperforms the state-of-the-art
without increasing complexity. The key concept behind BundleMoCap is manifold
interpolation between latent keyframes. By relying on a local manifold
smoothness assumption, we can efficiently solve a bundle of frames using a
single code. Additionally, the method can be implemented as a sliding window
optimization and requires only the first frame to be properly initialized,
reducing the overall computational burden. BundleMoCap's strength lies in its
ability to achieve high-quality motion capture results with simplicity and
efficiency. More details can be found at https://moverseai.github.io/bundle/.