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Data-driven grasp synthesis using shape matching and task-based pruning.

Ying Li1, Jiaxin L Fu, Nancy S Pollard

  • 1School of Computer Science, Robotics Institute NSH, Carnege Mellon University, Pittsburgh, PA 15213-3890, USA. liyingus@yahoo.com

IEEE Transactions on Visualization and Computer Graphics
|May 15, 2007
PubMed
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Animating human grasps is challenging. This study introduces a data-driven method using shape matching and quality measures to synthesize natural hand grasps for new objects, aiding animation and virtual environments.

Area of Science:

  • Robotics and Human-Computer Interaction
  • Computer Graphics and Animation

Background:

  • Animating human grasps, particularly whole-hand grasps, is complex due to the hand's high degrees of freedom and the need for natural object conformity.
  • Existing methods rely on captured human motion data but struggle with adapting grasps to novel objects.

Purpose of the Study:

  • To develop a data-driven approach for synthesizing natural human grasps for arbitrary objects.
  • To enable automatic grasp synthesis in virtual environments and assist animators in posing hands.

Main Methods:

  • A novel shape matching algorithm identifies candidate grasps by comparing hand and object features (relative placements, surface normals).
  • Candidate grasps are clustered and pruned using an anatomically-based grasp quality measure tailored for the human hand.

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Main Results:

  • The algorithm successfully synthesizes grasps for objects not present in the original human grasp database.
  • Demonstrated examples showcase the effectiveness of the data-driven grasp synthesis approach.

Conclusions:

  • The proposed method provides an effective solution for automatic grasp synthesis.
  • This technique is valuable for both animation tools and applications requiring automatic grasp generation in virtual environments.