这篇论文用原子动作来编舞，比直接生成连续动作更可控，效果也更自然，做AI舞蹈生成可以看看。
该研究提出结构感知框架，将舞蹈建模为原子动作序列。通过分割大规模舞蹈数据并聚类得到原子动作词汇，利用大语言模型进行语义重标和聚类优化。框架包含两阶段：先规划原子动作的类型、时长与时机，再生成平滑连贯的运动。实验在结构连贯性、节奏对齐和感知自然度上显著优于现有基线。
Music-to-Dance Generation via Atomic Movements
Music-driven dance generation aims to produce human motion that is both rhythmically synchronized and semantically consistent with music. While recent neural approaches have achieved impressive visual realism, they typically model motion as a continuous signal and neglect its compositional nature, making generated dances structurally incoherent and difficult to control. In this work, we introduce a structure-aware framework that models choreography as a sequence of atomic movements-semantically interpretable motion events that serve as the building blocks of dance. To construct this atomic movement vocabulary, we first segment large-scale dance data and cluster them into atomic movement groups. We then employ a large language model to semantically relabel and refine the clusters, yielding a set of interpretable and reusable atomic movements. Based on these atomic movement annotations, we design a two-stage generation framework that mirrors the human choreography process. In the atomic movement planning stage, the model predicts the type, duration, and timing of atomic movements conditioned on the input music, forming a symbolic dance allocation. In the completion stage, a transition-aware generator synthesizes smooth and stylistically coherent motion conditioned on the planned structure. Extensive experiments demonstrate that our method produces dances with significantly improved structural coherence, rhythmic alignment, and perceptual naturalness compared to existing baselines, while providing enhanced interpretability and controllable editing through explicit structural representation.