A Cross-Dataset Study for Text-based 3D Human Motion Retrieval
<|reference_start|>A Cross-Dataset Study for Text-based 3D Human Motion Retrieval: 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.<|reference_end|>
@article{bensabath2024a,
    title={A Cross-Dataset Study for Text-based 3D Human Motion Retrieval},
    author={L'eore Bensabath, Mathis Petrovich, G"ul Varol},
    journal={arXiv preprint arXiv:2405.16909},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.16909},
    primaryClass={cs.CV}
}