# BeatDance: A Beat-Based Model-Agnostic Contrastive Learning Framework for Music-Dance Retrieval

arXiv: https://arxiv.org/abs/2310.10300 · HTML: https://mlsanity.com/paper/2310.10300/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2023.

## Built on by (most cited first, 12 of 12)

- [HarmonySet: A Comprehensive Dataset for Understanding Video-Music Semantic Alignment and Temporal Synchronization](https://mlsanity.com/paper/2503.01725/) (2025)
- [FlowerDance: MeanFlow for Efficient and Refined 3D Dance Generation](https://mlsanity.com/paper/2511.21029/) (2025)
- [MACE-Dance: Motion-Appearance Cascaded Experts for Music-Driven Dance Video Generation](https://mlsanity.com/paper/2512.18181/) (2025)
- [PersonaGesture: Single-Reference Co-Speech Gesture Personalization for Unseen Speakers](https://mlsanity.com/paper/2605.06064/) (2026)
- [TokenDance: Token-to-Token Music-to-Dance Generation with Bidirectional Mamba](https://mlsanity.com/paper/2603.27314/) (2026)
- [OmniDance: Multimodal Driven Dance Video Generation with Large-scale Internet Data](https://mlsanity.com/paper/2606.30019/) (2026)
- [Not All Frames Are Equal: Complexity-Aware Masked Motion Generation via Motion Spectral Descriptors](https://mlsanity.com/paper/2603.29655/) (2026)
- [BiTDiff: Fine-Grained 3D Conducting Motion Generation via BiMamba-Transformer Diffusion](https://mlsanity.com/paper/2604.04395/) (2026)
- [Learning Quantised Structure-Preserving Motion Representations for Dance Fingerprinting](https://mlsanity.com/paper/2604.00927/) (2026)
- [Interactive Multi-Turn Retrieval for Health Videos](https://mlsanity.com/paper/2605.01409/) (2026)
- [StreamTalk: Streaming Co-Speech Gesture Generation with Key-Pose Anchoring](https://mlsanity.com/paper/2608.01643/) (2026)
- [TBSG-Net: Temporal Bipartite Scene Graph Network for Fine-Grained Video Moment Retrieval](https://mlsanity.com/paper/2608.02056/) (2026)

## Abstract (arXiv)

Dance and music are closely related forms of expression, with mutual retrieval between dance videos and music being a fundamental task in various fields like education, art, and sports. However, existing methods often suffer from unnatural generation effects or fail to fully explore the correlation between music and dance. To overcome these challenges, we propose BeatDance, a novel beat-based model-agnostic contrastive learning framework. BeatDance incorporates a Beat-Aware Music-Dance InfoExtractor, a Trans-Temporal Beat Blender, and a Beat-Enhanced Hubness Reducer to improve dance-music retrieval performance by utilizing the alignment between music beats and dance movements. We also introduce the Music-Dance (MD) dataset, a large-scale collection of over 10,000 music-dance video pairs for training and testing. Experimental results on the MD dataset demonstrate the superiority of our method over existing baselines, achieving state-of-the-art performance. The code and dataset will be made public available upon acceptance.