In this paper, we present TMR, a simple yet effective approach for text to 3D human motion retrieval. While previous work has only treated retrieval as a proxy evaluation metric, we tackle it as a standalone task. Our method extends the state-of-the-art text-to-motion synthesis model TEMOS, and incorporates a contrastive loss to better structure the cross-modal latent space. We show that maintaining the motion generation loss, along with the contrastive training, is crucial to obtain good performance. We introduce a benchmark for evaluation and provide an in-depth analysis by reporting results on several protocols. Our extensive experiments on the KIT-ML and HumanML3D datasets show that TMR outperforms the prior work by a significant margin, for example reducing the median rank from 54 to 19. Finally, we showcase the potential of our approach on moment retrieval. Our code and models are publicly available at https://mathis.petrovich.fr/tmr.

翻译：本文提出了TMR，一种简单而有效的文本到3D人体运动检索方法。此前研究仅将检索视为替代性评估指标，而我们将其作为独立任务处理。本方法基于最先进的文本-运动合成模型TEMOS，并引入对比损失函数以更好地构建跨模态潜在空间。研究表明，在对比训练过程中保持运动生成损失对获得优异性能至关重要。我们建立了评估基准，并通过多种协议下的结果报告进行了深入分析。在KIT-ML和HumanML3D数据集上的大量实验表明，TMR显著优于先前工作，例如将中位数排名从54降低至19。最后，我们展示了该方法在时刻检索中的应用潜力。代码与模型已公开于https://mathis.petrovich.fr/tmr。