research-article

Controllable Group Choreography Using Contrastive Diffusion

Authors:

Erman Tjiputra,

Anh NguyenAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 42, Issue 6

Article No.: 224, Pages 1 - 14

https://doi.org/10.1145/3618356

Published: 05 December 2023 Publication History

Abstract

Music-driven group choreography poses a considerable challenge but holds significant potential for a wide range of industrial applications. The ability to generate synchronized and visually appealing group dance motions that are aligned with music opens up opportunities in many fields such as entertainment, advertising, and virtual performances. However, most of the recent works are not able to generate high-fidelity long-term motions, or fail to enable controllable experience. In this work, we aim to address the demand for high-quality and customizable group dance generation by effectively governing the consistency and diversity of group choreographies. In particular, we utilize a diffusion-based generative approach to enable the synthesis of flexible number of dancers and long-term group dances, while ensuring coherence to the input music. Ultimately, we introduce a Group Contrastive Diffusion (GCD) strategy to enhance the connection between dancers and their group, presenting the ability to control the consistency or diversity level of the synthesized group animation via the classifier-guidance sampling technique. Through intensive experiments and evaluation, we demonstrate the effectiveness of our approach in producing visually captivating and consistent group dance motions. The experimental results show the capability of our method to achieve the desired levels of consistency and diversity, while maintaining the overall quality of the generated group choreography.

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Cited By

Wang ZLi JQin XSun SZhou SJia JLuo JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)DanceCamAnimator: Keyframe-Based Controllable 3D Dance Camera SynthesisProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680980(10200-10209)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680980
Le NDo KBui XDo TTjiputra ETran QNguyen A(2024)Scalable Group Choreography via Variational Phase Manifold LearningComputer Vision – ECCV 202410.1007/978-3-031-72649-1_17(293-311)Online publication date: 30-Sep-2024
https://doi.org/10.1007/978-3-031-72649-1_17

Index Terms

Controllable Group Choreography Using Contrastive Diffusion
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
  2. Modeling and simulation
    1. Simulation types and techniques
      1. Simulation by animation

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 42, Issue 6

December 2023

1565 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3632123

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Published in TOG Volume 42, Issue 6

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Cited By

Wang ZLi JQin XSun SZhou SJia JLuo JCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)DanceCamAnimator: Keyframe-Based Controllable 3D Dance Camera SynthesisProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680980(10200-10209)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680980
Le NDo KBui XDo TTjiputra ETran QNguyen A(2024)Scalable Group Choreography via Variational Phase Manifold LearningComputer Vision – ECCV 202410.1007/978-3-031-72649-1_17(293-311)Online publication date: 30-Sep-2024
https://doi.org/10.1007/978-3-031-72649-1_17

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