Learning skills from human motions offers a promising path toward generalizable policies for versatile humanoid whole-body control, yet two key cornerstones are missing: (1) a high-quality motion tracking framework that faithfully transforms large-scale kinematic references into robust and extremely dynamic motions on real hardware, and (2) a distillation approach that can effectively learn these motion primitives and compose them to solve downstream tasks. We address these gaps with BeyondMimic, a real-world framework to learn from human motions for versatile and naturalistic humanoid control via guided diffusion. Our framework provides a motion tracking pipeline capable of challenging skills such as jumping spins, sprinting, and cartwheels with state-of-the-art motion quality. Moving beyond simply mimicking existing motions, we further introduce a unified diffusion policy that enables zero-shot task-specific control at test time using simple cost functions. Deployed on hardware, BeyondMimic performs diverse tasks at test time, including waypoint navigation, joystick teleoperation, and obstacle avoidance, bridging sim-to-real motion tracking and flexible synthesis of human motion primitives for whole-body control. https://beyondmimic.github.io/.
