MotionHarness: Agentic Spatiotemporal Coordination for Human Motion Generation

Pretrained motion models provide expressive control over individual motions, yet this control alone does not ensure spatiotemporal coordination among multiple characters and objects. We propose MotionHarness, an agentic framework that jointly plans behavior, trajectories, and timing without task-specific retraining of the motion backbone. Event specifications make the required spatial and temporal relations explicit and guide verification of execution outcomes. History-guided search explores alternative plans and selectively recombines locally successful decisions from prior executions. To adapt transferred decisions to revised execution contexts, the framework invalidates affected evidence, rebinds transferred targets, and performs dependency-aware motion regeneration. This separates reuse of coordination decisions from reuse of their original trajectories and verification outcomes. We also introduce MotionCoordBench, a benchmark of 300 tasks spanning ten coordination families and three difficulty levels, with offline and online generation settings. Evaluation separately measures task satisfaction, motion validity, and computational cost. Evaluation on offline-generation tasks shows that MotionHarness improves both task success and validity-constrained task success over direct generation across three language-model backbones.