SCA 08: Eurographics/SIGGRAPH Symposium on Computer Animation
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Browsing SCA 08: Eurographics/SIGGRAPH Symposium on Computer Animation by Subject "Categories and Subject Descriptors (according to ACM CCS): I.3.7 [Three-Dimensional Graphics and Realism]: Animation"
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Item Achieving Good Connectivity in Motion Graphs(The Eurographics Association, 2008) Zhao, Liming; Safonova, Alla; Markus Gross and Doug JamesMotion graphs provide users with a simple yet powerful way to synthesize human motions. While motion graphbased synthesis has been widely successful, the quality of the generatedmotion depends largely on the connectivity of the graph and the quality of transitions in it. However, achieving both of these criteria simultaneously in motion graphs is difficult. Good connectivity requires transitions between less similar poses, while good motion quality results only when transitions happen between very similar poses. This paper introduces a new method for building motion graphs. The method first builds a set of interpolated motion clips, which contain many more similar poses than the original dataset. Using this set, the method then constructs a motion graph and reduces its size by minimizing the number of interpolated poses present in the graph. The outcome of the algorithm is a motion graph called a well-connected motion graph with very good connectivity and only smooth transitions. Our experimental results show that well-connected motion graphs outperform standardmotion graphs across a number of measures, result in very good motion quality, allow for high responsiveness when used for interactive control, and even do not require post-processing of the synthesizedmotionsItem Motion-Motif Graphs(The Eurographics Association, 2008) Beaudoin, Philippe; Coros, Stelian; Panne, Michiel van de; Poulin, Pierre; Markus Gross and Doug JamesWe present a technique to automatically distill a motion-motif graph from an arbitrary collection of motion capture data. Motion motifs represent clusters of similar motions and together with their encompassing motion graph they lend understandable structure to the contents and connectivity of large motion datasets. They can be used in support of motion compression, the removal of redundant motions, and the creation of blend spaces. This paper develops a string-based motif-finding algorithm which allows for a user-controlled compromise between motif length and the number of motions in a motif. It allows for time warps within motifs and assigns the majority of the input data to relevant motifs. Results are demonstrated for large datasets (more than 100,000 frames) with computation times of tens of minutes.