---
title: "基于原子运动的音乐到舞蹈生成框架"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.13978v1"
permalink: "https://feed.astralor.com/items/itm_9dab8691cf13d1673ac165c5"
published: "2026-07-15T16:03:55.000Z"
score: 79
channel: "ai"
tags: ["arXiv","两阶段生成","原子运动","大语言模型","舞蹈生成","音乐驱动"]
---

# 基于原子运动的音乐到舞蹈生成框架

## AI 点评

> 这篇论文值得关注，因为它为音乐驱动舞蹈生成提供了全新的结构视角——原子运动作为语义单元，显著提升了生成的可控性和可解释性。对于从事AI生成内容、虚拟人动画和交互式编舞的研究者尤其重要。

## 速览

该研究提出一种结构感知的舞蹈生成框架，将舞蹈编排建模为一系列语义可解释的原子运动序列。通过大规模舞蹈数据分割与聚类构建原子运动词汇，并利用大语言模型进行语义重标定。采用两阶段生成：原子运动规划阶段根据音乐预测动作类型、时长与时机；补全阶段生成平滑连贯的运动。实验表明该方法在结构连贯性、节奏对齐和感知自然度上显著优于基线，且支持可控编辑。

## 关键点

- 提出原子运动概念，将舞蹈分解为可复用的语义单元
- 利用大语言模型对聚类后的运动片段进行语义标注与精炼
- 两阶段生成框架：规划阶段与补全阶段，模拟人类编舞过程
- 在结构连贯性、节奏对齐和自然度上超越现有方法

## 全文

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.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_9dab8691cf13d1673ac165c5