Story-to-Motion is a novel task that takes a story (top green area) and generates motion and trajectories consistent with the textual description. The system utilizes modern large language models as a text-driven motion scheduler, extracting a series of (text, location) pairs from long texts. It also develops a text-driven motion retrieval scheme, combining classic motion matching with motion semantics and trajectory constraints. Furthermore, it designs a progressive masking transformer to address common problems in transition motions, such as unnatural poses and sliding. The system excels in three different subtasks: trajectory following, temporal action combination, and action mixing, outperforming previous motion synthesis methods.