Human animation from 2D correspondence based on motion trend prediction

Authors:
Li Zhang;Ling Li
Affiliations:
Department of Computing, Curtin University of Technology, Perth, WA;Department of Computing, Curtin University of Technology, Perth, WA
Venue:
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Year:
2006

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Abstract

A model-based method is proposed in this paper for 3-dimensional human motion recovery, taking un-calibrated monocular data as input. Motion trend prediction is suggested to to recover smooth human motions with high efficiency; while its outputs are guaranteed to not only resemble the original motion from the same viewpoint the sequence was taken, but also look natural and reasonable from any given viewpoint. To evaluate the accuracy of reconstruction algorithm, the research program starts from "synthesized" input. Experiment carried on real video data will be discussed as well, which indicate that the proposed method is able to recover smooth human motions from their 2D image features with high accuracy.