  3D models of humans are commonly used within computer graphics and vision,
and so the ability to distinguish between body shapes is an important shape
retrieval problem. We extend our recent paper which provided a benchmark for
testing non-rigid 3D shape retrieval algorithms on 3D human models. This
benchmark provided a far stricter challenge than previous shape benchmarks. We
have added 145 new models for use as a separate training set, in order to
standardise the training data used and provide a fairer comparison. We have
also included experiments with the FAUST dataset of human scans. All
participants of the previous benchmark study have taken part in the new tests
reported here, many providing updated results using the new data. In addition,
further participants have also taken part, and we provide extra analysis of the
retrieval results. A total of 25 different shape retrieval methods.
