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Data-driven motion reconstruction using local regression models
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posted on 2023-06-08, 18:20 authored by Christos Mousas, Paul Newbury, Christos-Nikolaos AnagnostopoulosReconstructing 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.
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Publication status
- Published
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Page range
364-374Presentation Type
- paper
Event name
10th International Conference Artificial Intelligence Applications and InnovationsEvent location
Rhodes, Greece.Event type
conferenceEvent date
19th-21st September 2014Department affiliated with
- Informatics Publications
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- No
Peer reviewed?
- Yes
Legacy Posted Date
2014-09-22Usage metrics
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