Abstract
Prior to reusing existing data, the most fundamental task is to understand the essence of the data. Furthermore, observing given data from distinct perspectives will provide different insights. Therefore, we seek to extract informative knowledge of animations for obtaining better clarity and usability. In this dissertation, we classify various animation features into two kinds of characteristics, representative attributes and implicit knowledge. Representative attributes denote the elements that can convey visual characteristics of animations, and implicit knowledge refers to those correlations and rules which potentially exist but are not presently evident.To obtain representative attributes, we present two animation summarization methods for extracting the keyframes of animations. The first approach is a constraint-based technique, called Key Probe, which casts the keyframe extraction problem as a constrained matrix factorization problem and solves the problem based on the least-squares optimization technique. The second method, called FrameRank, considers the frame dependent properties to evaluate frame importance and extract salient frames. Experiments with various types of animation examples show that the proposed method produces satisfying results. This method improves upon previous work in that it can deal with multiple objects, handle both rigid-body and soft-body animations, and cope with more complex animations that have a time-varying structure (e.g., fractures and fireworks).To discover the implicit knowledge of human motions, we present a novel visual analysis approach for revealing the relation between individual motions of different body portions. The kernel of our analysis method is a symbolic encoding scheme that represents the motion patterns with verbal symbols, effectively characterizing the spatio-temporal variation of the given motion. Two new visual representations are built on the basis of the verbal symbols: a motion plant that sequentially provides a detailed characterization of the motion, and a motion icon that statistically summarizes the comparisons between verbal streams of body portions. Experimental results show that our approach not only reveals the implicit relationships of human motions, but also favors comparative visualization of human motions in the context of the motion synergy study.