Lodge++: High-Quality and Long Dance Generation With Robust Choreography Patterns

We propose Lodge++, a choreography framework to generate high-quality, ultra-long dances given the music and desired genre. To handle the challenges in computational efficiency, the complex global choreography patterns across various dance genres, 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. Code, model and demonstrative video results will be available at https://li-ronghui.github.io/lodgepp.

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Publication Details

Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
2025-01-01
DOI
https://doi.org/10.1109/tpami.2025.3618016
Citations
2
Primary Topic
Human Motion and Animation
Type
article
Field-Weighted Citation Impact
2.43
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Lodge++: High-Quality and Long Dance Generation With Robust Choreography Patterns

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Lodge++: High-Quality and Long Dance Generation With Robust Choreography Patterns

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article en
2 citations

Abstract

We propose Lodge++, a choreography framework to generate high-quality, ultra-long dances given the music and desired genre. To handle the challenges in computational efficiency, the complex global choreography patterns across various dance genres, 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. Code, model and demonstrative video results will be available at https://li-ronghui.github.io/lodgepp.

IEEE Transactions on Pattern Analysis and Machine IntelligenceVol. PP
TH Bingen University of Applied Sciences (DE), Beijing Normal University (CN), University Town of Shenzhen (CN), Peng Cheng Laboratory (CN), Tsinghua University (CN)
Openalex Percentile: Top 10%
Human Motion and Animation
2.43
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