Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives
CVPR 2024(2024)
摘要
We propose Lodge, a network capable of generating extremely long dance
sequences conditioned on given music. We design Lodge as a two-stage coarse to
fine diffusion architecture, and propose the characteristic dance primitives
that possess significant expressiveness as intermediate representations between
two diffusion models. The first stage is global diffusion, which focuses on
comprehending the coarse-level music-dance correlation and production
characteristic dance primitives. In contrast, the second-stage is the local
diffusion, which parallelly generates detailed motion sequences under the
guidance of the dance primitives and choreographic rules. In addition, we
propose a Foot Refine Block to optimize the contact between the feet and the
ground, enhancing the physical realism of the motion. Our approach can
parallelly generate dance sequences of extremely long length, striking a
balance between global choreographic patterns and local motion quality and
expressiveness. Extensive experiments validate the efficacy of our method.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要