Grasp synthesis from low-dimensional probabilistic grasp models

Heni Ben Amor, Guido Heumer, Bernhard Jung, Arnd Vitzthum

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


We propose a novel data-driven animation method for the synthesis of natural looking human grasping. Motion data captured from human grasp actions is used to train a probabilistic model of the human grasp space. This model greatly reduces the high number of degrees of freedom of the human hand to a few dimensions in a continuous grasp space. The low dimensionality of the grasp space in turn allows for efficient optimization when synthesizing grasps for arbitrary objects. The method requires only a short training phase with no need for preprocessing of graphical objects for which grasps are to be synthesized.

Original languageEnglish (US)
Pages (from-to)445-454
Number of pages10
JournalComputer Animation and Virtual Worlds
Issue number3-4
StatePublished - Aug 2008
Externally publishedYes


  • Gaussian mixture models
  • Grasp synthesis
  • Principal component analysis

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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