Abstract. Volumetric structures are frequently used as shape descriptors for 3D data. The capture of such data is being facilitated by developments in multi-view video and range scanning, extending to subjects that are alive and moving. In this paper, we examine vision-based modeling and the related representation of moving articulated creatures using spines. We define a spine as a branching axial structure representing the shape and topology of a 3D object's limbs, and capturing the limbs' correspondence and motion over time. Our spine concept builds on skeletal representations often used to describe the internal structure of an articulated object and the significant protrusions. The algorithms for determining both 2D and 3D skeletons generally use an objective function tuned to balance stability against the responsiveness to detail. Our representation of a spine provides for enhancements over a 3D skeleton, afforded by temporal robustness and correspondence. We also introduce a probabilistic framework that is needed to compute the spine from a sequence of surface data. We present a practical implementation that approximates the spine's joint probability function to reconstruct spines for synthetic and real subjects that move.
The skeleton of a volume is defined as a 1D structure representing the shape and topology of the volume data REF , and it is a concise representation of shapes.
Novel Skeletal Representation for Articulated Creatures
ECCV (3)
Computer Science