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Due to recent advances in pose-estimation methods, human motion can beextracted from a common video in the form of 3D skeleton sequences. Despitewonderful application opportunities, effective and efficient content-basedaccess to large volumes of such spatio-temporal skeleton data still remains achallenging problem. In this paper, we propose a novel content-basedtext-to-motion retrieval task, which aims at retrieving relevant motions basedon a specified natural-language textual description. To define baselines forthis uncharted task, we employ the BERT and CLIP language representations toencode the text modality and successful spatio-temporal models to encode themotion modality. We additionally introduce our transformer-based approach,called Motion Transformer (MoT), which employs divided space-time attention toeffectively aggregate the different skeleton joints in space and time. Inspiredby the recent progress in text-to-image/video matching, we experiment with twowidely-adopted metric-learning loss functions. Finally, we set up a commonevaluation protocol by defining qualitative metrics for assessing the qualityof the retrieved motions, targeting the two recently-introduced KITMotion-Language and HumanML3D datasets. The code for reproducing our results isavailable at https://github.com/mesnico/text-to-motion-retrieval.

Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion Data and Natural Language