From Canonical Poses to 3D Motion Capture Using a Single Camera

  • Authors:
  • Andrea Fossati;Miodrag Dimitrijevic;Vincent Lepetit;Pascal Fua

  • Affiliations:
  • Ecole Polytechnique Fédérale de Lausanne (EPLFL/IC/ISIM/CVLab), Lausanne;Ecole Polytechnique Fédérale de Lausanne (EPLFL/IC/ISIM/CVLab), Lausanne;Ecole Polytechnique Fédérale de Lausanne (EPLFL/IC/ISIM/CVLab), Lausanne;Ecole Polytechnique Fédérale de Lausanne (EPLFL/IC/ISIM/CVLab), Lausanne

  • Venue:
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Year:
  • 2010

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Abstract

We combine detection and tracking techniques to achieve robust 3D motion recovery of people seen from arbitrary viewpoints by a single and potentially moving camera. We rely on detecting key postures, which can be done reliably, using a motion model to infer 3D poses between consecutive detections, and finally refining them over the whole sequence using a generative model. We demonstrate our approach in the cases of golf motions filmed using a static camera and walking motions acquired using a potentially moving one. We will show that our approach, although monocular, is both metrically accurate because it integrates information over many frames and robust because it can recover from a few misdetections.