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arXiv · 2604.03799

Next-Scale Autoregressive Models for Text-to-Motion Generation

Zhiwei Zheng·Shibo Jin·Lingjie Liu·Mingmin Zhao
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Abstract

Autoregressive (AR) models offer stable and efficient training, but standard next-token prediction is not well aligned with the temporal structure required for text-conditioned motion generation. We introduce MoScale, a next-scale AR framework that generates motion hierarchically from coarse to fine temporal resolutions. By providing global semantics at the coarsest scale and refining them progressively, MoScale establishes a causal hierarchy better suited for long-range motion structure. To improve robustness under limited text-motion data, we further incorporate cross-scale hierarchical refinement for improving per-scale initial predictions and in-scale temporal refinement for selective bidirectional re-prediction. MoScale achieves SOTA text-to-motion performance with high training efficiency, scales effectively with model size, and generalizes zero-shot to diverse motion generation and editing tasks.

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Zhiwei Zheng, Shibo Jin, Lingjie Liu, Mingmin Zhao. 2026-04-04. Next-Scale Autoregressive Models for Text-to-Motion Generation. https://arxiv.org/abs/2604.03799

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