TR2001-38

Flexible flow for 3D nonrigid tracking and shape recovery


    •  Brand, M.E.; Bhotika, R., "Flexible Flow for 3D Nonrigid Tracking and Shape Recovery", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ISSN: 1063-6919, December 2001, vol. 1, pp. 315-322.
      BibTeX Download PDF
      • @inproceedings{Brand2001dec2,
      • author = {Brand, M.E. and Bhotika, R.},
      • title = {Flexible Flow for 3D Nonrigid Tracking and Shape Recovery},
      • booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
      • year = 2001,
      • volume = 1,
      • pages = {315--322},
      • month = dec,
      • issn = {1063-6919},
      • url = {http://www.merl.com/publications/TR2001-38}
      • }
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  • Research Area:

    Algorithms


We introduce linear methods for model-based tracking of nonrigid 3D objects and for acquiring such models from video. 3D motions and flexions are calculated directly from image intensities without information-lossy intermediate results. Measurement uncertainty is quantified and fully propagated through the inverse model to yield posterior mean (PM) and mode (MAP) pose estimates. A Bayesian framework manages uncertainty, accommodates priors, and gives confidence measures. We obtain highly accurate and robust closed-form estimators by minimizing information loss from non-reversible (inner-product and least-squares) operations, and, when unavoidable, performing such operations with the appropriate error norm. For model acquisition, we show how to refine a crude or generic model to fit the video subject. We demonstrate with tracking, model refinement, and super-resolution texture lifting from low-quality low-resolution video.