An Eigen-based motion retrieval method for real-time animation

• A motion retrieval method in the transform domain instead of the time domain.
• Matching in eigenspaces, which are transformed from six sets of feature matrices.
• Efficient way to compute the matching score based on eigenvector correlation.
• Without time-series matching, more efficient and suitable for real-time animation.

Research on real-time 3D animation is attracting a lot of attention in recent years due to the popularity of emerging applications such as distributed virtual environments and computer games. One of the important issues in real-time animation is that the existing motion retrieval techniques generally have a high matching time because they are typically based on matching time-series, making them less suitable for use with large motion databases. In this paper, we propose a different approach to motion retrieval, called Eigen-based Motion Retrieval (or EigenMR), to address this limitation of the existing methods by performing motion retrieval in the transform domain instead of the time domain. To differentiate the motion of different body parts, we propose to perform the matching on individual body parts as well as on the whole body. Our approach has the important advantage that each body part can be represented by an index of fixed size, consisting of a number of eigenvectors and the corresponding eigenvalues. As a result, our approach has constant time complexity based on the number of motion files in the database instead of the size of the database. The experimental results show that our approach is both efficient and accurate compared with some of the latest methods. When applied to a motion database of 4 GB in size, our method requires approximately 20% of the standard time, making it more suitable for real-time animation.

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