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Motion Capture Similarity Analysis: Visualizing and Improving Metrics

Explore and enhance motion capture similarity metrics using Li's metric, visualize results, and plan future improvements in an InfoVis environment. Analyze various dimensionality reduction techniques on CMU MoCap Database with walking, running, jumping, boxing, and cartwheel sequences. Project includes detailed comparison of existing metrics, their strengths, and weaknesses, aiming to aid motion data understanding.

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Motion Capture Similarity Analysis: Visualizing and Improving Metrics

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  1. MotionVis Donovan Parks

  2. Outline • Project motivation and goal • Details of projects • Video showing results • Future work and conclusions

  3. Motivation • Large motion capture DB’s widely used in the film and video game industries • This has created a desire to be able to search these databases for logically similar motions

  4. Project Goal • Numerous similarity metrics have been proposed: • Which of these should be preferred? • What are their respective strengths and weaknesses? • How can a given metric be improved? • Develop an environment for analyzing the structure of a motion capture DB under a given similarity metric

  5. Project Overview • InfoVis environment for visualizing MoCap DB under a given similarity metric

  6. CMU MoCap Database • Publicly available database of MoCap data (mocap.cs.cmu.edu) • Project considers a subset of the CMU database • 110 walking sequences • 45 running sequences • 18 jumping sequences • 5 boxing sequences • 3 cartwheel sequences

  7. Li’s Similarity Metric • Treat each frame as a point in high-d space • Hypothesis: Similar motions will have a similar principal axis as determined by PCA • Angle between principal axes is used as the similarity measure Li and Prabhakaran (2006)

  8. Dimensionality Reduction • Three dimensionality techniques considered: • PCA / Classic MDS (linear, fast) • Metric MDS (nonlinear, slow) • Non-metric MDS (rank order, slow) • With 2 dimensions: • Classic MDS has a stress of ~0.08 • Metric MDS has a stress of ~0.05 • Non-metric MDS has a stress of ~0.03

  9. Visual encodings

  10. Future Work • Fix various short-comings of current implementation • Consider other MoCap similarity metrics • Dealing with data that has an intrinsic dimensionality > 2

  11. Conclusions • Environment for aiding understanding of a MoCap-based similarity metrics • Provides information about a similarity metric that is hard to obtain from: • analyzing numerical results • existing visualization environments

  12. Literature • Implemented similarity metric: • Chuanjun Li and B. Prabhakaran. Indexing of motion capture data for efficient and fast similarity search, 2006. • Other similarity metrics: • Lucas Kovar and Michael Gleicher. Automated extraction and parameterization of motions in large data sets. ACM Trans. Graph., 23(3):559568, 2004. • Meinard Müller, Tido Röder, and Michael Clausen. Efficient content-based retrieval of motion capture data. ACM Trans. Graph., 24(3):677685, 2005. • Related InfoVis papers: • Chris Roussin Rich DeJordy, Stephen P. Borgatti and Daniel S. Halgin. Visualizing proximity data, 2007. • Jonathan C. Roberts. State of the art: coordinated and multiple views in exploratory visualization. Proc. Conference on Coordinated and Multiple Views in Exploratory Visualization, 2007.

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