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Articulated Motion Capture from 3-D Points and Normals

Matti Niskanen 1 Edmond Boyer 2 Radu Horaud 2 
2 PERCEPTION - Interpretation and Modelling of Images and Videos
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : In this paper we address the problem of tracking the motion of articulated objects from their 2-D silhouettes gathered with several cameras. The vast majority of existing approaches relies on a single camera or on stereo. We describe a new method which requires at least two cameras. The method relies on (i) building 3-D observations (points and normals) from image silhouettes and on (ii) fitting an articulated object model to these observations by minimizing their discrepancies. The objective function sums up these discrepancies while it takes into account both the scaled algebraic distance from data points to the model surface and the offset in orientation between observed normals and model normals. The combination of a feed-forward reconstruction technique with a robust model-tracking method results in a reliable and efficient method for articulated motion capture.
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Submitted on : Tuesday, May 3, 2011 - 9:32:55 AM
Last modification on : Tuesday, May 17, 2022 - 9:08:01 AM
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  • HAL Id : inria-00590195, version 1



Matti Niskanen, Edmond Boyer, Radu Horaud. Articulated Motion Capture from 3-D Points and Normals. British Machine Vision Conference (BMVC '05), Sep 2005, Oxford, United Kingdom. pp.439--448. ⟨inria-00590195⟩



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