G2E-T2M: Global-to-Event Hierarchical Conditioning for Compositional Text-to-Motion Generation

Text-to-motion (T2M) models have achieved strong caption-level alignment, yet they often fail to realize individual actions in complex multi-event descriptions. Through extractive event-level analysis of standard T2M benchmarks, we observe that descriptions containing more constituent events are generally associated with longer motions and degraded generation performance. Motivated by these observations, we propose $\text{G2E-T2M}$, a Global-to-Event hierarchical conditioning framework for compositional T2M generation. G2E-T2M first conditions motion features on the full-caption tokens through global cross-attention and then performs independent token-level cross-attention with each constituent event. The resulting event-wise feature updates are adaptively aggregated at each motion position according to motion–event relevance and a weak order prior, separating fine-grained interaction within each event from competition across events. In addition to the standard generation objective, we introduce boundary-free event-wise semantic supervision that aligns each constituent event with semantically compatible local motion regions through multi-scale temporal search without requiring event-level temporal annotations. For event-level evaluation, we introduce Event MM-Dist with average- and worst-event aggregation to measure constituent-event alignment, derive event-order consistency from localized event–motion matches, and employ a VLM-based score to assess event realization. Experiments across HumanML3D, KIT-ML, and SnapMoGen demonstrate consistent improvements in both caption- and event-level semantic alignment while maintaining competitive motion quality across different levels of event complexity.