# Beat-It: Beat-Synchronized Multi-Condition 3D Dance Generation

arXiv: https://arxiv.org/abs/2407.07554 · HTML: https://mlsanity.com/paper/2407.07554/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2024.

## Built on by (most cited first, 24 of 24)

- [Motion Anything: Any to Motion Generation](https://mlsanity.com/paper/2503.06955/) (2025)
- [Align Your Rhythm: Generating Highly Aligned Dance Poses with Gating-Enhanced Rhythm-Aware Feature Representation](https://mlsanity.com/paper/2503.17340/) (2025)
- [ViBES: A Conversational Agent with Behaviorally-Intelligent 3D Virtual Body](https://mlsanity.com/paper/2512.14234/) (2025)
- [DanceChat: Large Language Model-Guided Music-to-Dance Generation](https://mlsanity.com/paper/2506.10574/) (2025)
- [StableGuard: Towards Unified Copyright Protection and Tamper Localization in Latent Diffusion Models](https://mlsanity.com/paper/2509.17993/) (2025)
- [PhysiInter: Integrating Physical Mapping for High-Fidelity Human Interaction Generation](https://mlsanity.com/paper/2506.07456/) (2025)
- [PAMD: Plausibility-Aware Motion Diffusion Model for Long Dance Generation](https://mlsanity.com/paper/2505.20056/) (2025)
- [OmniMotion: Multimodal Motion Generation with Continuous Masked Autoregression](https://mlsanity.com/paper/2510.14954/) (2025)
- [Tempo as the Stable Cue: Hierarchical Mixture of Tempo and Beat Experts for Music to 3D Dance Generation](https://mlsanity.com/paper/2512.18804/) (2025)
- [NECromancer: Breathing Life into Skeletons via BVH Animation](https://mlsanity.com/paper/2602.06548/) (2026)
- [SCJD: Sparse Correlation and Joint Distillation for Efficient 3D Human Pose Estimation](https://mlsanity.com/paper/2503.14097/) (2025)
- [EgoMusic-driven Human Dance Motion Estimation with Skeleton Mamba](https://mlsanity.com/paper/2508.10522/) (2025)
- [Think2Sing: Orchestrating Structured Motion Subtitles for Singing-Driven 3D Head Animation](https://mlsanity.com/paper/2509.02278/) (2025)
- [Learning Human Motion with Temporally Conditional Mamba](https://mlsanity.com/paper/2510.12573/) (2025)
- [MotionRAG-Diff: A Retrieval-Augmented Diffusion Framework for Long-Term Music-to-Dance Generation](https://mlsanity.com/paper/2506.02661/) (2025)
- [PaperFit: Vision-in-the-Loop Typesetting Optimization for Scientific Documents](https://mlsanity.com/paper/2605.10341/) (2026)
- [DiscoForcing: A Unified Framework for Real-Time Audio-Driven Character Control with Diffusion Forcing](https://mlsanity.com/paper/2605.28491/) (2026)
- [Wan-Dancer: A Hierarchical Framework for Minute-scale Coherent Music-to-Dance Generation](https://mlsanity.com/paper/2607.09581/) (2026)
- [Music-to-Dance Generation via Atomic Movements](https://mlsanity.com/paper/2607.13978/) (2026)
- [Action Dubber: Timing Audible Actions via Inflectional Flow](https://mlsanity.com/paper/2506.13320/) (2025)
- [Listen to Rhythm, Choose Movements: Autoregressive Multimodal Dance Generation via Diffusion and Mamba with Decoupled Dance Dataset](https://mlsanity.com/paper/2601.03323/) (2026)
- [Skeleton2Stage: Reward-Guided Fine-Tuning for Physically Plausible Dance Generation](https://mlsanity.com/paper/2602.13778/) (2026)
- [Not Like Transformers: Drop the Beat Representation for Dance Generation with Mamba-Based Diffusion Model](https://mlsanity.com/paper/2603.08023/) (2026)
- [Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation](https://mlsanity.com/paper/2606.22726/) (2026)

## Abstract (arXiv)

Dance, as an art form, fundamentally hinges on the precise synchronization with musical beats. However, achieving aesthetically pleasing dance sequences from music is challenging, with existing methods often falling short in controllability and beat alignment. To address these shortcomings, this paper introduces Beat-It, a novel framework for beat-specific, key pose-guided dance generation. Unlike prior approaches, Beat-It uniquely integrates explicit beat awareness and key pose guidance, effectively resolving two main issues: the misalignment of generated dance motions with musical beats, and the inability to map key poses to specific beats, critical for practical choreography. Our approach disentangles beat conditions from music using a nearest beat distance representation and employs a hierarchical multi-condition fusion mechanism. This mechanism seamlessly integrates key poses, beats, and music features, mitigating condition conflicts and offering rich, multi-conditioned guidance for dance generation. Additionally, a specially designed beat alignment loss ensures the generated dance movements remain in sync with the designated beats. Extensive experiments confirm Beat-It's superiority over existing state-of-the-art methods in terms of beat alignment and motion controllability.