  In computer graphics, animation compression is essential for efficient
storage, streaming and reproduction of animated meshes. Previous work has
presented efficient techniques for compression by deriving skinning
transformations and weights using clustering of vertices based on geometric
features of vertices over time. In this work we present a novel approach that
assigns vertices to bone-influenced clusters and derives weights using deep
learning through a training set that consists of pairs of vertex trajectories
(temporal vertex sequences) and the corresponding weights drawn from fully
rigged animated characters. The approximation error of the resulting linear
blend skinning scheme is significantly lower than the error of competent
previous methods by producing at the same time a minimal number of bones.
Furthermore, the optimal set of transformation and vertices is derived in fewer
iterations due to the better initial positioning in the multidimensional
variable space. Our method requires no parameters to be determined or tuned by
the user during the entire process of compressing a mesh animation sequence.
