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.
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.364-374, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_36〉
https://hal.inria.fr/hal-01391338
Contributeur : Hal Ifip
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Dernière modification le : mercredi 14 février 2018 - 11:46:01
Document(s) archivé(s) le : samedi 4 février 2017 - 13:29:29
Christos Mousas, Paul Newbury, Christos-Nikolaos Anagnostopoulos. Data-Driven Motion Reconstruction Using Local Regression Models. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.364-374, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_36〉. 〈hal-01391338〉
