Abstract
A novel motion retrieval approach based on statistical learning and Bayesian fusion is presented. The approach includes two primary stages. (1) In the learning stage, fuzzy clustering is utilized firstly to get the representative frames of motions, and the gesture features of the motions are extracted to build a motion feature database. Based on the motion feature database and statistical learning, the probability distribution function of different motion classes is obtained. (2) In the motion retrieval stage, the query motion feature is extracted firstly according to stage (1). Similarity measurements are then conducted employing a novel method that combines category-based motion similarity distances with similarity distances based on canonical correlation analysis. The two motion distances are fused using Bayesian estimation, and the retrieval results are ranked according to the fused values. The effectiveness of the proposed method is verified experimentally.
Highlights
In recent years, computer animation has become increasing employed in various applications [1,2,3,4,5,6,7,8]
The application of computer animation to human motion is of particular interest
Motion retrieval has recently turned into a main research focus in the field of Motion capture (MoCap) animation
Summary
Computer animation has become increasing employed in various applications [1,2,3,4,5,6,7,8]. The application of computer animation to human motion is of particular interest. This has led to a high demand for producing very realistic representations of human movement. Many approaches have been developed to generate human motion data. Motion capture (MoCap) is a well-known method. The increasing availability of MoCap devices has driven the development of large human and object motion databases [8, 9]. Motion retrieval has recently turned into a main research focus in the field of MoCap animation
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