# Align Your Rhythm: Generating Highly Aligned Dance Poses with Gating-Enhanced Rhythm-Aware Feature Representation

arXiv: https://arxiv.org/abs/2503.17340 · HTML: https://mlsanity.com/paper/2503.17340/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 45 references that are in the map.
- First posted on arXiv 21 Mar 2025.

## Builds on (most cited first, 26 of 26)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [EDGE: Editable Dance Generation From Music](https://mlsanity.com/paper/2211.10658/) (2022)
- [Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory](https://mlsanity.com/paper/2203.13055/) (2022)
- [Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models](https://mlsanity.com/paper/2211.09707/) (2022)
- [MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training](https://mlsanity.com/paper/2306.00107/) (2023)
- [Motion Mamba: Efficient and Long Sequence Motion Generation](https://mlsanity.com/paper/2403.07487/) (2024)
- [Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain](https://mlsanity.com/paper/2203.17004/) (2022)
- [Human Motion Generation: A Survey](https://mlsanity.com/paper/2307.10894/) (2023)
- [FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generation](https://mlsanity.com/paper/2212.03741/) (2022)
- [Learning to Generate Diverse Dance Motions with Transformer](https://mlsanity.com/paper/2008.08171/) (2020)
- [Dance Revolution: Long-Term Dance Generation with Music via Curriculum Learning](https://mlsanity.com/paper/2006.06119/) (2020)
- [Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives](https://mlsanity.com/paper/2403.10518/) (2024)
- [Beat-It: Beat-Synchronized Multi-Condition 3D Dance Generation](https://mlsanity.com/paper/2407.07554/) (2024)
- [DiffDance: Cascaded Human Motion Diffusion Model for Dance Generation](https://mlsanity.com/paper/2308.02915/) (2023)
- [InfiniMotion: Mamba Boosts Memory in Transformer for Arbitrary Long Motion Generation](https://mlsanity.com/paper/2407.10061/) (2024)
- [Light-T2M: A Lightweight and Fast Model for Text-to-motion Generation](https://mlsanity.com/paper/2412.11193/) (2024)
- [GTN-Bailando: Genre Consistent Long-Term 3D Dance Generation based on Pre-trained Genre Token Network](https://mlsanity.com/paper/2304.12704/) (2023)
- [Explore 3D Dance Generation via Reward Model from Automatically-Ranked Demonstrations](https://mlsanity.com/paper/2312.11442/) (2023)
- [FTMoMamba: Motion Generation with Frequency and Text State Space Models](https://mlsanity.com/paper/2411.17532/) (2024)
- [Enhancing Expressiveness in Dance Generation via Integrating Frequency and Music Style Information](https://mlsanity.com/paper/2403.05834/) (2024)

## Built on by (most cited first, 8 of 8)

- [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)
- [DiscoForcing: A Unified Framework for Real-Time Audio-Driven Character Control with Diffusion Forcing](https://mlsanity.com/paper/2605.28491/) (2026)
- [Music-to-Dance Generation via Atomic Movements](https://mlsanity.com/paper/2607.13978/) (2026)
- [OpenDance: Multimodal Controllable 3D Dance Generation with Large-scale Internet Data](https://mlsanity.com/paper/2506.07565/) (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)

Automatically generating natural, diverse and rhythmic human dance movements driven by music is vital for virtual reality and film industries. However, generating dance that naturally follows music remains a challenge, as existing methods lack proper beat alignment and exhibit unnatural motion dynamics. In this paper, we propose Danceba, a novel framework that leverages gating mechanism to enhance rhythm-aware feature representation for music-driven dance generation, which achieves highly aligned dance poses with enhanced rhythmic sensitivity. Specifically, we introduce Phase-Based Rhythm Extraction (PRE) to precisely extract rhythmic information from musical phase data, capitalizing on the intrinsic periodicity and temporal structures of music. Additionally, we propose Temporal-Gated Causal Attention (TGCA) to focus on global rhythmic features, ensuring that dance movements closely follow the musical rhythm. We also introduce Parallel Mamba Motion Modeling (PMMM) architecture to separately model upper and lower body motions along with musical features, thereby improving the naturalness and diversity of generated dance movements. Extensive experiments confirm that Danceba outperforms state-of-the-art methods, achieving significantly better rhythmic alignment and motion diversity. Project page: https://danceba.github.io/ .