Mousas, Christos, Newbury, Paul and Anagnostopoulos, Christos-Nikolaos (2014) Data-driven motion reconstruction using local regression models. In: 10th International Conference Artificial Intelligence Applications and Innovations, 19th-21st September 2014, Rhodes, Greece..

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Abstract

Reconstructing human motion data using a few input signals or trajectories is always challenging problem. This is due to the difficulty of reconstructing natural human motion since the low-dimensional control parameters cannot be directly used to reconstruct the high-dimensional human motion. Because of this limitation, a novel methodology is introduced in this paper that takes benefit of local dimensionality reduction techniques to reconstruct accurate and natural-looking full-body motion sequences using fewer number of input. In the proposed methodology, a group of local dynamic regression models is formed from pre-captured motion data to support the prior learning process that reconstructs the full-body motion of the character. The evaluation that held out has shown that such a methodology can reconstruct more accurate motion sequences than possible with other statistical models.

Item Type:	Conference or Workshop Item (Paper)
Schools and Departments:	School of Engineering and Informatics > Informatics
Subjects:	Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Depositing User:	Christos Mousas
Date Deposited:	22 Sep 2014 10:10
Last Modified:	22 Sep 2014 10:10
URI:	http://sro.sussex.ac.uk/id/eprint/50111

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