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Orthopedists invest significant amounts of effort and time trying to understand the biomechanics of arthrodial (gliding) joints. Although new image acquisition and processing methods currently generate richer-than-ever geometry and kinematic data sets that are individual specific, the computational and visualization tools needed to enable the comparative analysis and exploration of these data sets lag behind. In this paper, we present a framework that enables the cross-data-set visual exploration and analysis of arthrodial joint biomechanics. Central to our approach is a computer-vision-inspired markerless method for establishing pairwise correspondences between individual-specific geometry. Manifold models are subsequently defined and deformed from one individual-specific geometry to another such that the markerless correspondences are preserved while minimizing model distortion. The resulting mutually consistent parameterization and visualization allow the users to explore the similarities and differences between two data sets and to define meaningful quantitative measures. We present two applications of this framework to human-wrist data: articular cartilage transfer from cadaver data to in vivo data and cross-data-set kinematics analysis. The method allows our users to combine complementary geometries acquired through different modalities and thus overcome current imaging limitations. The results demonstrate that the technique is useful in the study of normal and injured anatomy and kinematics of arthrodial joints. In principle, the pairwise cross-parameterization method applies to all spherical topology data from the same class and should be particularly beneficial in instances where identifying salient object features is a nontrivial task.