Bridging Semantic and Kinematic Conditions with Diffusion-based Discrete Motion Tokenizer

ArXi:2603.19227v1 Announce Type: new Prior motion generation largely follows two paradigms: continuous diffusion models that excel at kinematic control, and discrete token-based generators that are effective for semantic conditioning. To combine their strengths, we propose a three-stage framework comprising condition feature extraction (Perception), discrete token generation (Planning), and diffusion-based motion synthesis (Control