  In this paper, we address the unexplored question of temporal sentence
localization in human motions (TSLM), aiming to locate a target moment from a
3D human motion that semantically corresponds to a text query. Considering that
3D human motions are captured using specialized motion capture devices, motions
with only a few joints lack complex scene information like objects and
lighting. Due to this character, motion data has low contextual richness and
semantic ambiguity between frames, which limits the accuracy of predictions
made by current video localization frameworks extended to TSLM to only a rough
level. To refine this, we devise two novel label-prior-assisted training
schemes: one embed prior knowledge of foreground and background to highlight
the localization chances of target moments, and the other forces the originally
rough predictions to overlap with the more accurate predictions obtained from
the flipped start/end prior label sequences during recovery training. We show
that injecting label-prior knowledge into the model is crucial for improving
performance at high IoU. In our constructed TSLM benchmark, our model termed
MLP achieves a recall of 44.13 at<EMAIL_ADDRESS>on the BABEL dataset and 71.17 on
HumanML3D (Restore), outperforming prior works. Finally, we showcase the
potential of our approach in corpus-level moment retrieval. Our source code is
openly accessible at https://github.com/eanson023/mlp.
