Content-Based Cross Search for Human Motion Data using Time-Varying Mesh and Motion Capture Data

Abstract

This paper describes a content-based cross search scheme for two kinds of three-dimensional (3D) human motion data: time-varying mesh (TVM) and motion capture data. TVM is a sequence of 3D mesh models made for real-world 3D objects. TVM is generated using multiple-view images taken with multiple cameras. Since TVM can record shape, color, and motion of the real-world 3D objects, it has been drawing a lot of attention these days. In order to realize practical archiving systems for TVM, efficient retrieval systems are indispensable. The retrieval systems for TVM developed so far are based on query-by-example. This means additional TVM generation is required for constructing queries, which is computationally demanding and time consuming. On the other hand, motion capture systems are widely used to capture 3D human motion. However, the data structure is very different from that of TVM. Therefore, the two kinds of 3D human motion data are incompatible to each other. In this paper, we present a retrieval system that enables retrieving TVM using motion capture data as queries and vice versa using the modified shape distribution algorithm.