arXiv:2410.20389cs.CVcs.AI2024-10被引 30

生成超长高质量舞蹈,精准还原复杂编排和真实动作细节

Lodge++: High-quality and Long Dance Generation with Vivid Choreography Patterns

  • 分两阶段生成:先定全局编排框架,再细化高质量动作序列
  • 可生成长达数分钟的舞蹈,保持复杂编排与动作真实性
  • 适合影视、游戏中的角色舞蹈生成,对物理合理性有严格优化

我们提出Lodge++,一个基于音乐和目标舞种生成高质量、超长且富有表现力舞蹈的编排框架。为应对计算效率、复杂全局编排学习及局部动作物理质量的挑战,Lodge++采用从粗到精的两阶段策略:第一阶段设计全局编排网络,生成捕捉复杂全局编排模式的粗粒度舞蹈基元;第二阶段在基元引导下,通过基于基元的舞蹈扩散模型并行生成高质量长序列舞蹈,忠实遵循复杂编排结构。此外,为提升动作物理合理性,Lodge++引入穿透抑制模块解决角色自穿透问题,足部精细化模块优化脚地接触,以及多舞种判别器维持整段舞蹈的风格一致性。大量实验验证表明,该方法能快速生成适用于多种舞种的超长舞蹈,确保全局编排有序且局部动作质量高。

原文摘要 · Abstract (English)

We propose Lodge++, a choreography framework to generate high-quality, ultra-long, and vivid dances given the music and desired genre. To handle the challenges in computational efficiency, the learning of complex and vivid global choreography patterns, and the physical quality of local dance movements, Lodge++ adopts a two-stage strategy to produce dances from coarse to fine. In the first stage, a global choreography network is designed to generate coarse-grained dance primitives that capture complex global choreography patterns. In the second stage, guided by these dance primitives, a primitive-based dance diffusion model is proposed to further generate high-quality, long-sequence dances in parallel, faithfully adhering to the complex choreography patterns. Additionally, to improve the physical plausibility, Lodge++ employs a penetration guidance module to resolve character self-penetration, a foot refinement module to optimize foot-ground contact, and a multi-genre discriminator to maintain genre consistency throughout the dance. Lodge++ is validated by extensive experiments, which show that our method can rapidly generate ultra-long dances suitable for various dance genres, ensuring well-organized global choreography patterns and high-quality local motion.

舞蹈生成扩散模型编排控制动作真实

Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。