Data-Driven Motion Reconstruction Using Local Regression Models - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2014

Data-Driven Motion Reconstruction Using Local Regression Models

Christos Mousas
  • Function : Author
  • PersonId : 992474
Christos-Nikolaos Anagnostopoulos
  • Function : Author
  • PersonId : 992335


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.
Fichier principal
Vignette du fichier
978-3-662-44654-6_36_Chapter.pdf (556.41 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01391338 , version 1 (03-11-2016)





Christos Mousas, Paul Newbury, Christos-Nikolaos Anagnostopoulos. Data-Driven Motion Reconstruction Using Local Regression Models. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. pp.364-374, ⟨10.1007/978-3-662-44654-6_36⟩. ⟨hal-01391338⟩
127 View
122 Download



Gmail Facebook Twitter LinkedIn More