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Using nonlinear dimensionality reduction in 3D figure animation

Published: 18 March 2005 Publication History

Abstract

This paper explores a method for re-sequencing an existing set of animation, specifically motion capture data, to generate new motion. Re-using animation is helpful in designing virtual environments and creating video games for reasons of cost and efficiency. This paper demonstrates that through nonlinear dimensionality reduction and frame re-sequencing, visually compelling motion can be produced from a set of motion capture data. The technique presented uses Isomap and ST-Isomap to reduce the dimensionality of the data set. Two distance metrics for nonlinear dimensionality reduction are compared as well as the effect of global degrees of freedom on the visual appeal of the newly generated motion.

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Cited By

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  • (2019)Motion capture data segmentation using Riemannian manifold learningComputer Animation and Virtual Worlds10.1002/cav.188531:1Online publication date: 10-Jun-2019
  • (2016)Graph-based representation learning for automatic human motion segmentationMultimedia Tools and Applications10.1007/s11042-016-3480-575:15(9205-9224)Online publication date: 1-Aug-2016
  • (2015)Motion Key-Frame Extraction by Using Optimized t-Stochastic Neighbor EmbeddingSymmetry10.3390/sym70203957:2(395-411)Online publication date: 21-Apr-2015
  • Show More Cited By

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Published In

cover image ACM Conferences
ACMSE '05 vol 2: Proceedings of the 43rd annual ACM Southeast Conference - Volume 2
March 2005
430 pages
ISBN:1595930590
DOI:10.1145/1167253
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 March 2005

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Author Tags

  1. 3D figure animation
  2. dimensionality reduction
  3. motion capture
  4. re-sequencing

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ACM SE05
Sponsor:
ACM SE05: ACM Southeast Regional Conference 2005
March 18 - 20, 2005
Georgia, Kennesaw

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Overall Acceptance Rate 502 of 1,023 submissions, 49%

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Cited By

View all
  • (2019)Motion capture data segmentation using Riemannian manifold learningComputer Animation and Virtual Worlds10.1002/cav.188531:1Online publication date: 10-Jun-2019
  • (2016)Graph-based representation learning for automatic human motion segmentationMultimedia Tools and Applications10.1007/s11042-016-3480-575:15(9205-9224)Online publication date: 1-Aug-2016
  • (2015)Motion Key-Frame Extraction by Using Optimized t-Stochastic Neighbor EmbeddingSymmetry10.3390/sym70203957:2(395-411)Online publication date: 21-Apr-2015
  • (2014)A genetic algorithm approach to human motion capture data segmentationComputer Animation and Virtual Worlds10.1002/cav.159725:3-4(283-292)Online publication date: 1-May-2014
  • (2013)Human motion capture data segmentation based on graph partition2013 6th International Congress on Image and Signal Processing (CISP)10.1109/CISP.2013.6745223(1117-1121)Online publication date: Dec-2013

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