# DanceCrafter: Fine-Grained Text-Driven Controllable Dance Generation via Choreographic Syntax

arXiv: https://arxiv.org/abs/2604.18648 · HTML: https://mlsanity.com/paper/2604.18648/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Builds on (most cited first, 21 of 21)

- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations](https://mlsanity.com/paper/2301.06052/) (2023)
- [Executing your Commands via Motion Diffusion in Latent Space](https://mlsanity.com/paper/2212.04048/) (2022)
- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [The KIT Motion-Language Dataset](https://mlsanity.com/paper/1607.03827/) (2016)
- [Query-Key Normalization for Transformers](https://mlsanity.com/paper/2010.04245/) (2020)
- [Motion-X: A Large-scale 3D Expressive Whole-body Human Motion Dataset](https://mlsanity.com/paper/2307.00818/) (2023)
- [Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory](https://mlsanity.com/paper/2203.13055/) (2022)
- [Wan-Animate: Unified Character Animation and Replacement with Holistic Replication](https://mlsanity.com/paper/2509.14055/) (2025)
- [SAM 3D Body: Robust Full-Body Human Mesh Recovery](https://mlsanity.com/paper/2602.15989/) (2026)
- [SnapMoGen: Human Motion Generation from Expressive Texts](https://mlsanity.com/paper/2507.09122/) (2025)
- [Kimodo: Scaling Controllable Human Motion Generation](https://mlsanity.com/paper/2603.15546/) (2026)
- [HY-Motion 1.0: Scaling Flow Matching Models for Text-To-Motion Generation](https://mlsanity.com/paper/2512.23464/) (2025)
- [X-Dancer: Expressive Music to Human Dance Video Generation](https://mlsanity.com/paper/2502.17414/) (2025)
- [On Efficient and Statistical Quality Estimation for Data Annotation](https://mlsanity.com/paper/2405.11919/) (2024)
- [DanceEditor: Towards Iterative Editable Music-driven Dance Generation with Open-Vocabulary Descriptions](https://mlsanity.com/paper/2508.17342/) (2025)
- [From Generated Human Videos to Physically Plausible Robot Trajectories](https://mlsanity.com/paper/2512.05094/) (2025)
- [Music-Aligned Holistic 3D Dance Generation via Hierarchical Motion Modeling](https://mlsanity.com/paper/2507.14915/) (2025)
- [Walk Before You Dance: High-fidelity and Editable Dance Synthesis via Generative Masked Motion Prior](https://mlsanity.com/paper/2504.04634/) (2025)

## Abstract (arXiv)

Text-driven controllable dance generation remains under-explored, primarily due to the severe scarcity of high-quality datasets and the inherent difficulty of articulating complex choreographies. Characterizing dance is particularly challenging owing to its intricate spatial dynamics, strong directionality, and the highly decoupled movements of distinct body parts. To overcome these bottlenecks, we bridge principles from dance studies, human anatomy, and biomechanics to propose textitChoreographic Syntax, a novel theoretical framework with a tailored annotation system. Grounded in this syntax, we combine professional dance archives with high-fidelity motion capture data to construct DanceFlow, the most fine-grained dance dataset to date. It encompasses 41 hours of high-quality motions paired with 6.34 million words of detailed descriptions. At the model level, we introduce DanceCrafter, a tailored motion transformer built upon the Momentum Human Rig. To circumvent optimization instabilities, we construct a continuous manifold motion representation paired with a hybrid normalization strategy. Furthermore, we design an anatomy-aware loss to explicitly regulate the decoupled nature of body parts. Together, these adaptations empower DanceCrafter to achieve the high-fidelity and stable generation of complex dance sequences. Extensive evaluations and user studies demonstrate our state-of-the-art performance in motion quality, fine-grained controllability, and generation naturalness.