  With the release of large-scale motion datasets with textual annotations, the
task of establishing a robust latent space for language and 3D human motion has
recently witnessed a surge of interest. Methods have been proposed to convert
human motion and texts into features to achieve accurate correspondence between
them. Despite these efforts to align language and motion representations, we
claim that the temporal element is often overlooked, especially for compound
actions, resulting in chronological inaccuracies. To shed light on the temporal
alignment in motion-language latent spaces, we propose Chronologically Accurate
Retrieval (CAR) to evaluate the chronological understanding of the models. We
decompose textual descriptions into events, and prepare negative text samples
by shuffling the order of events in compound action descriptions. We then
design a simple task for motion-language models to retrieve the more likely
text from the ground truth and its chronologically shuffled version. CAR
reveals many cases where current motion-language models fail to distinguish the
event chronology of human motion, despite their impressive performance in terms
of conventional evaluation metrics. To achieve better temporal alignment
between text and motion, we further propose to use these texts with shuffled
sequence of events as negative samples during training to reinforce the
motion-language models. We conduct experiments on text-motion retrieval and
text-to-motion generation using the reinforced motion-language models, which
demonstrate improved performance over conventional approaches, indicating the
necessity to consider temporal elements in motion-language alignment.
