用扩散模型生成紧凑动作令牌，兼顾语义控制与运动精度。
Bridging Semantic and Kinematic Conditions with Diffusion-based Discrete Motion Tokenizer
- 分三阶段：感知、规划、控制，用扩散令牌解耦语义与细节。
- 仅用1/6令牌，轨迹误差降为0.08厘米，FID降至0.029。
- 强运动约束下仍提升精度，适合高精度动作生成场景。
以往动作生成主要分为连续扩散模型（擅长运动控制）和离散令牌生成器（擅长语义条件）。为结合二者优势，本文提出三阶段框架：感知、规划、控制。核心是MoTok——一种基于扩散的离散动作令牌化器，通过将运动重建交由扩散解码器，实现语义抽象与精细重建解耦，支持紧凑单层令牌同时保持运动保真度。运动控制中，粗粒度约束用于规划阶段，细粒度约束通过扩散优化在控制阶段施加，避免运动细节干扰语义规划。在HumanML3D数据集上，相比MaskControl，本方法使用六分之一令牌，轨迹误差从0.72厘米降至0.08厘米，FID从0.083降至0.029。当施加强运动约束时，其他方法性能下降，而本方法仍提升精度，FID从0.033降至0.014。
原文摘要 · Abstract (English)
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). Central to this framework is MoTok, a diffusion-based discrete motion tokenizer that decouples semantic abstraction from fine-grained reconstruction by delegating motion recovery to a diffusion decoder, enabling compact single-layer tokens while preserving motion fidelity. For kinematic conditions, coarse constraints guide token generation during planning, while fine-grained constraints are enforced during control through diffusion-based optimization. This design prevents kinematic details from disrupting semantic token planning. On HumanML3D, our method significantly improves controllability and fidelity over MaskControl while using only one-sixth of the tokens, reducing trajectory error from 0.72 cm to 0.08 cm and FID from 0.083 to 0.029. Unlike prior methods that degrade under stronger kinematic constraints, ours improves fidelity, reducing FID from 0.033 to 0.014.
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