Hierarchical Indexing Structure for 3D Human Motions Gaurav N. Pradhan, Chuanjun Li, and Balakrishnan Prabhakaran Department of Computer Science University of Texas at Dallas, Richardson, TX 75083 {gnp021000, chuanjun, praba}@utdallas.edu Abstract. Content-based retrieval of 3D human motion capture data has significant impact in different fields such as physical medicine, re- habilitation, and animation. This paper develops an efficient indexing approach for 3D motion capture data, supporting queries involving both sub-body motions (e.g., Find similar knee motions) as well as whole-body motions. The proposed indexing structure is based on the hierarchical structure of the human body segments consisting of independent index trees corresponding to each sub-part of the body. Each level of every index tree is associated with the weighted feature vectors of a body seg- ment and supports queries on sub-body motions and also on whole-body motions. Experiments show that up to 97% irrelevant motions can be pruned for any kind of motion query while retrieving all similar motions, and one traversal of the index structure through all index trees takes on an average 15 μsec with the existence of motion variations. 1 Introduction Several scientific applications, especially those in medical and security field, need to analyze and quantify the complex human body motions. Sophisticated motion capture facilities aid in representing the complex human motion in the 3D space. The 3D human joint data from motion capture facility helps in analysis and comparison of the motions. Focus of the Paper: Our main objective of this paper is to find similar 3D human motions by constructing the indexing structure which supports queries on sub-body motions in addition to whole-body motions. We focus on content- based retrieval for the sub-body queries such as Find similar shoulder motions, Find similar leg motions etc., or more regular query on whole body such as Find similar walking human motion. Some of the major challenges in indexing large 3D human motion databases are: – 3D motions are multi-dimensional, multi-attribute and co-related in nature; associated segments of one sub-body (e.g. hand) must be processed always together along every dimension. – Human motions exhibit huge variations in speed for similar motions as well as in directionality. T.-J. Cham et al. (Eds.): MMM 2007, LNCS 4351, Part I, pp. 386–396, 2007. c  Springer-Verlag Berlin Heidelberg 2007