PartiGen: Part-Aware Representations for Language-Conditioned Humanoid Motion Generation

Translating language into humanoid motion requires coordinating body parts, determining motion duration, and producing references suitable for robot execution. However, existing text-to-motion methods have limitations in whole-body coordination and duration adaptation, while the physical constraints of humanoid robots are not always explicitly considered. We introduce PartiGen, a part-aware latent diffusion framework that incorporates anatomical structure into a shared whole-body motion representation. Part-specific attention processes anatomically masked frame features, and its outputs are fused to combine local limb information with whole-body context. Conditioned on text and motion history, the model generates motion primitives autoregressively. Forward-kinematics supervision encourages kinematic consistency, while a learned end-of-sequence predictor selects a frame-level endpoint after generation. On HumanML3D retargeted to the Unitree G1, PartiGen reduces segment-level FID by 16.8% over TextOp and improves duration accuracy over a fixed-length cutoff. With a pretrained, frozen tracking controller, we evaluate generated references in MuJoCo and demonstrate execution of selected motions on the real G1 after simulation screening and manual selection.