Tempo as the Stable Cue: Hierarchical Mixture of Tempo and Beat Experts for Music to 3D Dance Generation
Abstract
Music to 3D dance generation aims to synthesize realistic and rhythmically synchronized human dance from music. While existing methods often rely on additional genre labels to further improve dance generation, such labels are typically noisy, coarse, unavailable, or insufficient to capture the diversity of real-world music, which can result in rhythm misalignment or stylistic drift. In contrast, we observe that tempo, a core property reflecting musical rhythm and pace, remains relatively consistent across datasets and genres, typically ranging from 60 to 200 BPM. Based on this finding, we propose TempoMoE, a hierarchical tempo-aware Mixture-of-Experts module that enhances the diffusion model and its rhythm perception. TempoMoE organizes motion experts into tempo-structured groups for different tempo ranges, with multi-scale beat experts capturing fine- and long-range rhythmic dynamics. A Hierarchical Rhythm-Adaptive Routing dynamically selects and fuses experts from music features, enabling flexible, rhythm-aligned generation without manual genre labels. Extensive experiments demonstrate that TempoMoE achieves state-of-the-art results in dance quality and rhythm alignment.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel hierarchical Mixture-of-Experts approach for generating dance movements from music, significantly advancing the state-of-the-art in rhythm-aware dance generation. The methodology is innovative, although the lack of reproducibility details and practical applications may limit its immediate impact in the field.
Methodology
The proposed TempoMoE framework introduces a novel hierarchical Mixture-of-Experts approach that effectively organizes motion experts based on tempo, allowing for improved rhythm perception in dance generation. The multi-scale beat experts enhance the model's ability to capture both fine and long-range rhythmic dynamics, which is a significant advancement over traditional methods that rely on genre labels. The dynamic routing mechanism for selecting and fusing experts based on music features is particularly innovative and addresses the limitations of existing approaches.
Results & evaluation
The experiments conducted are extensive and demonstrate the effectiveness of TempoMoE in achieving state-of-the-art results in both dance quality and rhythm alignment. The evaluation metrics used are appropriate, and the comparisons with baseline models are well-articulated, showcasing the advantages of the proposed method. However, more details on the datasets used and their diversity would strengthen the evaluation.
Reproducibility
The paper lacks sufficient implementation details and code availability, which raises concerns about reproducibility. Clear guidelines and access to the model and datasets would enhance the ability of other researchers to replicate the results.
Limitations
One limitation is the reliance on tempo as the primary cue, which may not capture all aspects of musical expression. Additionally, the hierarchical structure may introduce complexity that could hinder real-time applications. The absence of a demo or project URL also limits practical engagement with the research.
Broader impact
This research has significant implications for the fields of music and dance, potentially enabling more sophisticated applications in entertainment, virtual reality, and interactive media. The ability to generate dance movements that are rhythmically aligned with music could enhance user experiences in various digital platforms. The paper presents a novel hierarchical Mixture-of-Experts approach for generating dance movements from music, significantly advancing the state-of-the-art in rhythm-aware dance generation. The methodology is innovative, although the lack of reproducibility details and practical applications may limit its immediate impact in the field.
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