MotionSCM: Structural Causal Modeling for Interventional Human Motion Generation

Existing human motion control interfaces specify semantic or kinematic properties without exposing cross-joint coordination as an independently editable mechanism. This makes it difficult to isolate an action's dependence on a particular coordination rule and trace how changes to that rule propagate across the body. We introduce $\text{MotionSCM}$, a motion generator for studying these dependencies through mechanism interventions. Its time-varying structural causal model uses explicit structural assignments to generate each motion channel. Under stated causal assumptions, post-training edits replace selected assignments while holding generator inputs and unedited mechanisms fixed. Per-channel gain constraints preserve rollout stability within the admissible operator set. On professional MoCap data from six industrial and craft processes, the learned structure varies across actions and individual motions. Interventions on the same rule produce distinct action-dependent cross-joint responses, with lagged recurrence sustaining the transmitted changes. MotionSCM retains competitive generation quality on three action-conditioned benchmarks. Structural ablations further show that improved generation fit need not preserve intervention behaviour. These results establish coordination as an explicit object of causal analysis within the motion generator. Code is available at https://anonymous.4open.science/r/MotionSCM-736C/.