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We propose a novel framework for comparing 3D human shapes under the changeof shape and pose. This problem is challenging since 3D human shapes varysignificantly across subjects and body postures. We solve this problem by usinga Riemannian approach. Our core contribution is the mapping of the human bodysurface to the space of metrics and normals. We equip this space with a familyof Riemannian metrics, called Ebin (or DeWitt) metrics. We treat a human bodysurface as a point in a "shape space" equipped with a family of Riemannianmetrics. The family of metrics is invariant under rigid motions andreparametrizations; hence it induces a metric on the "shape space" of surfaces.Using the alignment of human bodies with a given template, we show that thisfamily of metrics allows us to distinguish the changes in shape and pose. Theproposed framework has several advantages. First, we define a family of metricswith desired invariance properties for the comparison of human shape. Second,we present an efficient framework to compute geodesic paths between human shapegiven the chosen metric. Third, this framework provides some basic tools forstatistical shape analysis of human body surfaces. Finally, we demonstrate theutility of the proposed framework in pose and shape retrieval of human body.

A Riemannian Framework for Analysis of Human Body Surface