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.

翻译：本文提出TMR，一种简单而有效的文本到三维人体运动检索方法。不同于以往仅将检索作为代理评估指标的工作，我们将检索作为独立任务处理。该方法扩展了最先进的文本-运动合成模型TEMOS，并通过引入对比损失来更好地构建跨模态潜在空间。实验表明，在对比训练中保留运动生成损失对取得良好性能至关重要。我们建立了评估基准，并通过多协议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证明，TMR显著优于先前方法，例如将中位数排名从54降至19。最后，我们展示了该方法在时刻检索任务中的潜力。我们的代码和模型已公开。