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DEFORMABLE MOTION 3D RECONSTRUCTION BY UNION OF REGULARIZED SUBSPACES

Citation Author(s):
Antonio Agudo and Francesc Moreno-Noguer
Submitted by:
Antonio Agudo
Last updated:
5 October 2018 - 4:01pm
Document Type:
Poster
Document Year:
2018
Event:
Presenters:
Antonio Agudo
Paper Code:
ICIP18001
 

This paper presents an approach to jointly retrieve camera pose, time-varying 3D shape, and automatic clustering based on motion primitives, from incomplete 2D trajectories in a monocular video. We introduce the concept of order-varying temporal regularization in order to exploit video data, that can be indistinctly applied to the 3D shape evolution as well as to the similarities between images. This results in a union of regularized subspaces which effectively encodes the 3D shape deformation. All parameters are learned via augmented Lagrange multipliers, in a unified and unsupervised manner that does not assume any training data at all. Experimental validation is reported on human motion from sparse to dense shapes, providing more robust and accurate solutions than state-of-the-art approaches in terms of 3D reconstruction, while also obtaining motion grouping results.

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