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Related Experiment Video

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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

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

Summary

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.

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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.

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.