Datensatz

Zusammenfassung

Generation and animation of realistic humans is an essential part of many
projects in today’s media industry.
Especially, the games and special effects industry heavily depend on realistic
human animation. In this work a
unified model that describes both, human pose and body shape is introduced
which allows us to accurately model
muscle deformations not only as a function of pose but also dependent on the
physique of the subject. Coupled with
the model’s ability to generate arbitrary human body shapes, it severely
simplifies the generation of highly realistic
character animations. A learning based approach is trained on approximately 550
full body 3D laser scans taken
of 114 subjects. Scan registration is performed using a non-rigid deformation
technique. Then, a rotation invariant
encoding of the acquired exemplars permits the computation of a statistical
model that simultaneously encodes
pose and body shape. Finally, morphing or generating meshes according to
several constraints simultaneously
can be achieved by training semantically meaningful regressors.