Title
Cage-Based Motion Recovery Using Manifold Learning
Abstract
We present a flexible model-based approach for the recovery of parameterized motion from a sequence of 3D meshes without temporal coherence. Unlike previous model-based approaches using skeletons, we embed the deformation of a reference mesh template within a low polygonal representation of the mesh, namely the cage, using Green Coordinates. The advantage is a less constrained model that more robustly adapts to noisy observations while still providing structured motion information, as required by several applications. The cage is parameterized with a set of 3D features dedicated to the description of human morphology. This allows to formalize a novel representation of 3D meshed and articulated characters, the Oriented Quads Rigging (OQR). To regularize the tracking, the OQR space is subsequently constrained to plausible poses using manifold learning. Results are shown for sequences of meshes, with and without temporal coherence, obtained from multiple view videos preprocessed by visual hull. Motion recovery applications are illustrated with a motion transfer encoding and the extraction of trajectories of anatomical joints. Validation is performed on the Human Eva II database.
Year
DOI
Venue
2012
10.1109/3DIMPVT.2012.29
3DIMPVT
Keywords
Field
DocType
computer graphics,image motion analysis,learning (artificial intelligence),3D features,3D mesh sequence,3D meshed character representation,Green Coordinates,Human Eva II database,OQR,Oriented Quads Rigging,anatomical joints,articulated characters,cage-based motion recovery,flexible model-based approach,human morphology,low polygonal representation,manifold learning,motion recovery applications,motion transfer encoding,parameterized motion recovery,reference mesh template deformation,structured motion information,visual hull,dimension reduction,manifold learning,markerless motion capture,tracking
Computer vision,Parameterized complexity,Dimensionality reduction,Polygon mesh,Motion field,Visual hull,Computer science,Artificial intelligence,Motion estimation,Nonlinear dimensionality reduction,Computer graphics
Conference
Citations 
PageRank 
References 
4
0.40
17
Authors
4
Name
Order
Citations
PageRank
Estelle Duveau140.74
Simon Courtemanche240.40
Lionel Reveret31307.37
Edmond Boyer42758130.84