# GTN-Bailando: Genre Consistent Long-Term 3D Dance Generation based on Pre-trained Genre Token Network

arXiv: https://arxiv.org/abs/2304.12704 · HTML: https://mlsanity.com/paper/2304.12704/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2023.

## Built on by (most cited first, 10 of 10)

- [Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives](https://mlsanity.com/paper/2403.10518/) (2024)
- [Lodge++: High-quality and Long Dance Generation with Vivid Choreography Patterns](https://mlsanity.com/paper/2410.20389/) (2024)
- [Freetalker: Controllable Speech and Text-Driven Gesture Generation Based on Diffusion Models for Enhanced Speaker Naturalness](https://mlsanity.com/paper/2401.03476/) (2024)
- [UnifiedGesture: A Unified Gesture Synthesis Model for Multiple Skeletons](https://mlsanity.com/paper/2309.07051/) (2023)
- [The DiffuseStyleGesture+ entry to the GENEA Challenge 2023](https://mlsanity.com/paper/2308.13879/) (2023)
- [Align Your Rhythm: Generating Highly Aligned Dance Poses with Gating-Enhanced Rhythm-Aware Feature Representation](https://mlsanity.com/paper/2503.17340/) (2025)
- [TokenDance: Token-to-Token Music-to-Dance Generation with Bidirectional Mamba](https://mlsanity.com/paper/2603.27314/) (2026)
- [InfiniteDance: Scalable 3D Dance Generation Towards in-the-wild Generalization](https://mlsanity.com/paper/2603.13375/) (2026)
- [ChairPose: Pressure-based Chair Morphology Grounded Sitting Pose Estimation through Simulation-Assisted Training](https://mlsanity.com/paper/2508.01850/) (2025)
- [DanceDuo: Bridging Human Movement and AI Choreography](https://mlsanity.com/paper/2606.26507/) (2026)

## Abstract (arXiv)

Music-driven 3D dance generation has become an intensive research topic in recent years with great potential for real-world applications. Most existing methods lack the consideration of genre, which results in genre inconsistency in the generated dance movements. In addition, the correlation between the dance genre and the music has not been investigated. To address these issues, we propose a genre-consistent dance generation framework, GTN-Bailando. First, we propose the Genre Token Network (GTN), which infers the genre from music to enhance the genre consistency of long-term dance generation. Second, to improve the generalization capability of the model, the strategy of pre-training and fine-tuning is adopted.Experimental results on the AIST++ dataset show that the proposed dance generation framework outperforms state-of-the-art methods in terms of motion quality and genre consistency.