AI product Open source · MIT
UniMate is an AI research system for text-to-motion generation across diverse 3D skeletons, developed by researchers at Princeton, UC Berkeley, MIT, and NTU. Given a rigged asset and a text prompt, it generates articulated motion without per-skeleton retraining or test-time optimization.
The system is trained on the UniML3D dataset, which canonicalizes text-paired motion for bipedal, quadrupedal, avian, marine, insectoid, serpentine, and articulated rigid-object skeletons. Its training pipeline uses flow matching: a network predicts the velocity of a linear interpolation between noise and motion data, with masked L2, geodesic rotation, and velocity-smoothness losses. The model supports graph-based spatial and temporal attention or full attention over joint-time tokens, with text conditioning through adaptive layer normalization or cross-attention. At inference, classifier-free guidance generates motion features and can render skeleton animations or export animated GLB and FBX files.
The same trained model supports text-to-motion generation, motion in-betweening, text-guided joint-preserving editing, and expansion of sequences from multiple prompts. The repository includes dataset processing, training, inference, and preprocessing code, and is released under the MIT License. The included datasets and source assets remain subject to their original licenses.