  We provide results of our study on text-based 3D human motion retrieval and
particularly focus on cross-dataset generalization. Due to practical reasons
such as dataset-specific human body representations, existing works typically
benchmarkby training and testing on partitions from the same dataset. Here, we
employ a unified SMPL body format for all datasets, which allows us to perform
training on one dataset, testing on the other, as well as training on a
combination of datasets. Our results suggest that there exist dataset biases in
standard text-motion benchmarks such as HumanML3D, KIT Motion-Language, and
BABEL. We show that text augmentations help close the domain gap to some
extent, but the gap remains. We further provide the first zero-shot action
recognition results on BABEL, without using categorical action labels during
training, opening up a new avenue for future research.
