  We present a learning method for predicting animation skeletons for input 3D
models of articulated characters. In contrast to previous approaches that fit
pre-defined skeleton templates or predict fixed sets of joints, our method
produces an animation skeleton tailored for the structure and geometry of the
input 3D model. Our architecture is based on a stack of hourglass modules
trained on a large dataset of 3D rigged characters mined from the web. It
operates on the volumetric representation of the input 3D shapes augmented with
geometric shape features that provide additional cues for joint and bone
locations. Our method also enables intuitive user control of the
level-of-detail for the output skeleton. Our evaluation demonstrates that our
approach predicts animation skeletons that are much more similar to the ones
created by humans compared to several alternatives and baselines.
