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Cited by 342

The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.

Performance animation from low-dimensional control signals

Chai, Jinxiang, Hodgins, Jessica K.·2005·ACM Transactions on Graphics

Abstract

This paper introduces an approach to performance animation that employs video cameras and a small set of retro-reflective markers to create a low-cost, easy-to-use system that might someday be practical for home use. The low-dimensional control signals from the user's performance are supplemented by a database of pre-recorded human motion. At run time, the system automatically learns a series of local models from a set of motion capture examples that are a close match to the marker locations captured by the cameras. These local models are then used to reconstruct the motion of the user as a full-body animation. We demonstrate the power of this approach with real-time control of six different behaviors using two video cameras and a small set of retro-reflective markers. We compare the resulting animation to animation from commercial motion capture equipment with a full set of markers.

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Metadata

Authors
Chai, J., & Hodgins, J. K.
Venue
ACM Transactions on Graphics
Published
2005-07
Volume
24
Issue
3
Pages
686-696
ISSN
  • 0730-0301 (ISSN-L)
  • 1557-7368
OpenAlex
W1982905909
Cited by
342
References
48
FWCI
50.22
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Performance animation from low-dimensional control signals. https://app.cochat.ai/papers/10.1145/1073204.1073248
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Chai, J., & Hodgins, J. K. (2005). Performance animation from low-dimensional control signals. ACM Transactions on Graphics. https://doi.org/10.1145/1073204.1073248

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.