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Dettaglio pubblicazione

2016, Computer Vision – ECCV 2016, Pages 566-582 (volume: 9912)

Bayesian image based 3D pose estimation (04b Atto di convegno in volume)

Sanzari Marta, Ntouskos Valsamis, Pirri Ardizzone Maria Fiora

We introduce a 3D human pose estimation method from single image, based on a hierarchical Bayesian non-parametric model. The proposed model relies on a representation of the idiosyncratic motion of human body parts, which is captured by a subdivision of the human skeleton joints into groups. A dictionary of motion snapshots for each group is generated. The hierarchy ensures to integrate the visual features within the pose dictionary. Given a query image, the learned dictionary is used to estimate the likelihood of the group pose based on its visual features. The full-body pose is reconstructed taking into account the consistency of the connected group poses. The results show that the proposed approach is able to accurately reconstruct the 3D pose of previously unseen subjects
ISBN: 978-331946483-1
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