Human motion understanding is a fundamental task with diverse practical
applications, facilitated by the availability of large-scale motion capture
datasets. Recent studies focus on text-motion tasks, such as text-based motion
generation, editing and question answering. In this study, we introduce the
novel task of text-based human motion grounding (THMG), aimed at precisely
localizing temporal segments corresponding to given textual descriptions within
untrimmed motion sequences. Capturing global temporal information is crucial
for the THMG task. However, transformer-based models that rely on global
temporal self-attention face challenges when handling long untrimmed sequences
due to the quadratic computational cost. We address these challenges by
proposing Text-controlled Motion Mamba (TM-Mamba), a unified model that
integrates temporal global context, language query control, and spatial graph
topology with only linear memory cost. The core of the model is a
text-controlled selection mechanism which dynamically incorporates global
temporal information based on text query. The model is further enhanced to be
topology-aware through the integration of relational embeddings. For
evaluation, we introduce BABEL-Grounding, the first text-motion dataset that
provides detailed textual descriptions of human actions along with their
corresponding temporal segments. Extensive evaluations demonstrate the
effectiveness of TM-Mamba on BABEL-Grounding.

文本注意动作集成与全局时间信息，在处理传感器数据时，通过 TM-Mamba 模型能够高效地将人类运动与时间对应起来并进行理解。