# POPDG: Popular 3D Dance Generation with PopDanceSet

arXiv: https://arxiv.org/abs/2405.03178 · HTML: https://mlsanity.com/paper/2405.03178/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2024.

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

- [Lodge++: High-quality and Long Dance Generation with Vivid Choreography Patterns](https://mlsanity.com/paper/2410.20389/) (2024)
- [DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding](https://mlsanity.com/paper/2411.19527/) (2024)
- [X-Dancer: Expressive Music to Human Dance Video Generation](https://mlsanity.com/paper/2502.17414/) (2025)
- [MOSPA: Human Motion Generation Driven by Spatial Audio](https://mlsanity.com/paper/2507.11949/) (2025)
- [StableMotion: Training Motion Cleanup Models with Unpaired Corrupted Data](https://mlsanity.com/paper/2505.03154/) (2025)
- [DanceEditor: Towards Iterative Editable Music-driven Dance Generation with Open-Vocabulary Descriptions](https://mlsanity.com/paper/2508.17342/) (2025)
- [MDD: A Dataset for Text-and-Music Conditioned Duet Dance Generation](https://mlsanity.com/paper/2508.16911/) (2025)
- [PAMD: Plausibility-Aware Motion Diffusion Model for Long Dance Generation](https://mlsanity.com/paper/2505.20056/) (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)
- [TokenDance: Token-to-Token Music-to-Dance Generation with Bidirectional Mamba](https://mlsanity.com/paper/2603.27314/) (2026)
- [GCDance: Genre-Controlled Music-Driven 3D Full Body Dance Generation](https://mlsanity.com/paper/2502.18309/) (2025)
- [InfiniteDance: Scalable 3D Dance Generation Towards in-the-wild Generalization](https://mlsanity.com/paper/2603.13375/) (2026)
- [Wan-Dancer: A Hierarchical Framework for Minute-scale Coherent Music-to-Dance Generation](https://mlsanity.com/paper/2607.09581/) (2026)
- [OpenDance: Multimodal Controllable 3D Dance Generation with Large-scale Internet Data](https://mlsanity.com/paper/2506.07565/) (2025)
- [Reframing Music-Driven 2D Dance Pose Generation as Multi-Channel Image Generation](https://mlsanity.com/paper/2512.11720/) (2025)
- [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)
- [Generating Key Postures of Bharatanatyam Adavus with Pose Estimation](https://mlsanity.com/paper/2603.29570/) (2026)
- [TeMuDance: Contrastive Alignment-Based Textual Control for Music-Driven Dance Generation](https://mlsanity.com/paper/2604.17005/) (2026)
- [NRITYAM: Language Models Meet Art and Heritage of Dance](https://mlsanity.com/paper/2606.19727/) (2026)
- [Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation](https://mlsanity.com/paper/2606.22726/) (2026)
- [FlowDance: Music-Driven Dance Video Generation with Parallel Pose and RGB Streams](https://mlsanity.com/paper/2608.15818/) (2026)
- [BeatDance: Generating Beat-Consistent 3D Dance with Hierarchical Spatial-Temporal Modeling](https://mlsanity.com/paper/2609.37400/) (2026)
- [Sonic-AMG: Streaming Human Motion Generation from Spatial Audio](https://mlsanity.com/paper/2610.13300/) (2026)

## Abstract (arXiv)

Generating dances that are both lifelike and well-aligned with music continues to be a challenging task in the cross-modal domain. This paper introduces PopDanceSet, the first dataset tailored to the preferences of young audiences, enabling the generation of aesthetically oriented dances. And it surpasses the AIST++ dataset in music genre diversity and the intricacy and depth of dance movements. Moreover, the proposed POPDG model within the iDDPM framework enhances dance diversity and, through the Space Augmentation Algorithm, strengthens spatial physical connections between human body joints, ensuring that increased diversity does not compromise generation quality. A streamlined Alignment Module is also designed to improve the temporal alignment between dance and music. Extensive experiments show that POPDG achieves SOTA results on two datasets. Furthermore, the paper also expands on current evaluation metrics. The dataset and code are available at https://github.com/Luke-Luo1/POPDG.