Superquadrics are one of the ideal shape representations for adapting various kinds of primitive shapes with a single equation. This paper revisits the task of representing a 3D human body with multiple superquadrics. As a single superquadric surface can only represent symmetric primitive shapes, we present a method that segments the human body into body parts to estimate their superquadric parameters. Moreover, we propose a novel initial parameter estimation method by using 3D skeleton joints. The results show that superquadric parameters are estimated, which represent human body parts volumetrically.
CITATION STYLE
Hachiuma, R., & Saito, H. (2019). Volumetric Representation of Semantically Segmented Human Body Parts Using Superquadrics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11883 LNCS, pp. 52–61). Springer. https://doi.org/10.1007/978-3-030-31908-3_4
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