Modelling dynamic pose deformations of human subjects is an important topic in many research applications.
Existing approaches of human pose deformations can be classified as volume-based, skeletal animation and
example-based methods. These approaches have both strengths and limitations. However, for models in
customized shapes, it is very challenging to deform these models into different poses rapidly and realistically. We
10 propose a conceptual model to realize rapid and realistic pose deformation to customized human models by the
integration of skeletal-driven rigid deformation and example-learnt non-rigid surface deformation. Based on this
framework, a method for rapid automatic pose deformation is developed to deform human models of various
body shapes into a series of dynamic poses. A series of algorithms are proposed to complete the pose deformation
automatically and efficiently, including automatic segmentation of body parts and skeleton embedding, skeletal15
driven rigid deformation, training of non-rigid deformation from pose dataset; shape mapping of non-rigid
deformation, and integration of rigid and non-rigid deformations. Experiment has shown that the proposed
method can customize accurate human models based on two orthogonal-view photos and also efficiently generate
realistic pose deformations for the customized models
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