Skip to main content

Motion capture based motion analysis and motion synthesis for human-like character animation.

Xiao, Z., 2009. Motion capture based motion analysis and motion synthesis for human-like character animation. Doctoral Thesis (Doctoral). Bournemouth University.

Full text available as:




Motion capture technology is recognised as a standard tool in the computer animation pipeline. It provides detailed movement for animators; however, it also introduces problems and brings concerns for creating realistic and convincing motion for character animation. In this thesis, the post-processing techniques are investigated that result in realistic motion generation. Anumber of techniques are introduced that are able to improve the quality of generated motion from motion capture data, especially when integrating motion transitions from different motion clips. The presented motion data reconstruction technique is able to build convincing realistic transitions from existing motion database, and overcome the inconsistencies introduced by traditional motion blending techniques. It also provides a method for animators to re-use motion data more efficiently. Along with the development of motion data transition reconstruction, the motion capture data mapping technique was investigated for skeletal movement estimation. The per-frame based method provides animators with a real-time and accurate solution for a key post-processing technique. Although motion capture systems capture physically-based motion for character animation, no physical information is included in the motion capture data file. Using the knowledge of biomechanics and robotics, the relevant information for the captured performer are able to be abstracted and a mathematical-physical model are able to be constructed; such information is then applied for physics-based motion data correction whenever the motion data is edited.

Item Type:Thesis (Doctoral)
Group:Faculty of Media & Communication
ID Code:14590
Deposited On:20 May 2010 15:31
Last Modified:09 Aug 2022 16:02


Downloads per month over past year

More statistics for this item...
Repository Staff Only -