• DocumentCode
    2269727
  • Title

    A Data-driven Approach to Human-body Cloning Using a Segmented Body Database

  • Author

    Xi, Pengcheng ; Lee, Won-Sook ; Shu, Chang

  • Author_Institution
    Nat. Res. Council of Canada, Halifax
  • fYear
    2007
  • fDate
    Oct. 29 2007-Nov. 2 2007
  • Firstpage
    139
  • Lastpage
    147
  • Abstract
    We present a data-driven approach to build a human body model from a single photograph by performing Principal Component Analysis (PCA) on a database of body segments. We segment a collection of human bodies to compile the required database prior to performing the analysis. Our approach then builds a single PCA for each body segment - head, left and right arms, torso and left and right legs - yielding six PCAs in total. This strategy improves on the flexibility of conventional data-driven approaches in 3D modeling and allows our approach to take variations in ethnicity, age and body posture into account. We demonstrate our approach in practice by constructing models of a Caucasian male, an Asian male and a toddler from corresponding photographs and a Caucasian adult oriented database. We also discuss rapid consistent parameterization based on Radial Basis Functions (RBF) and non-optimization based learning systems to reduce execution time.
  • Keywords
    image reconstruction; learning (artificial intelligence); principal component analysis; radial basis function networks; solid modelling; visual databases; 3D modeling; PCA; data-driven approach; human-body cloning; image reconstruction; nonoptimization based learning system; principal component analysis; radial basis function; rapid consistent parameterization; segmented body database; Arm; Biological system modeling; Cloning; Databases; Humans; Leg; Pediatrics; Performance analysis; Principal component analysis; Torso;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Graphics and Applications, 2007. PG '07. 15th Pacific Conference on
  • Conference_Location
    Maui, HI
  • ISSN
    1550-4085
  • Print_ISBN
    978-0-7695-3009-3
  • Type

    conf

  • DOI
    10.1109/PG.2007.45
  • Filename
    4392724