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arXiv Computer Vision By Aidan Bradshaw, Marco Giordano, David Rode, Andreas Habersack, Elif Basokur, Annika Kruse, Markus Tilp, Michele Magno, Peter Wolf, Luca Benini, Christoph Leitner

MuyBridge: Mobile Human Center-of-Mass Estimation from Monocular Video via Sparse Fusion

Read the original on arXiv Computer Vision →

MuyBridge is an on-device system that estimates an athlete’s segmental center of mass (CoM) trajectory from a single phone camera video stream. It combines a compact 2D pose network with a distilled monocular depth network, fusing their outputs through anatomical and physical priors to produce metric CoM estimates without requiring 3D or task‑specific supervision. On the AthletePose3D dataset, MuyBridge achieves 33–41 mm vertical CoM error and 2.3–6.6 % absolute‑relative range error, delivering CoM estimates at 63 FPS on an iPhone 15 with asynchronous depth updates.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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