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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 Anagnostopoulos
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.

History

Publication status

  • Published

Page range

364-374

Presentation Type

  • paper

Event name

10th International Conference Artificial Intelligence Applications and Innovations

Event location

Rhodes, Greece.

Event type

conference

Event date

19th-21st September 2014

Department affiliated with

  • Informatics Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2014-09-22

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