Color 3D digital human modeling and its applications to animation and anthropometry

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Abstract

With the rapid advancement in laser technology, computer vision, and embedded computing, the application of laser scanning to the digitization of three dimensional physical realities has become increasingly widespread. In this paper, we focus on research results embodied in a 3D human body color digitization system developed at Tianjin University, and in collaboration with the Hong Kong University of Science and Technology. In digital human modeling, the first step involves the acquisition of the 3D human body data. We have over the years developed laser scanning technological know-how from first principles to support our research activities on building the first 3D digital human database for ethnic Chinese. The disadvantage of the conventional laser scanning is that surface color information is not contained in the point cloud data. By adding color imaging sensors to the developed multi-axis laser scanner, both the 3D human body coordinate data and the body surface color mapping are acquired. Our latest development is focused on skeleton extraction which is the key step towards human body animation, and applications to dynamic anthropometry. For dynamic anthropometric measurements, we first use an animation algorithm to adjust the 3D digital human to the required standard posture for measurement, and then fix the feature points and feature planes based on human body geometric characteristics. Utilizing the feature points, feature planes, and the extracted human body skeleton, we have measured 40 key sizes for the stand posture, and the squat posture. These experimental results will be given, and the factors that affect the measurement precision are analyzed through qualitative and quantitative analyses. © Springer-Verlag Berlin Heidelberg 2007.

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Ge, B. Z., Tian, Q. G., Young, K. D., & Sun, Y. C. (2007). Color 3D digital human modeling and its applications to animation and anthropometry. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4561 LNCS, pp. 82–91). Springer Verlag. https://doi.org/10.1007/978-3-540-73321-8_10

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