# InterDance:Reactive 3D Dance Generation with Realistic Duet Interactions

arXiv: https://arxiv.org/abs/2412.16982 · HTML: https://mlsanity.com/paper/2412.16982/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 53 references that are in the map.
- First posted on arXiv 22 Dec 2024.

## Builds on (most cited first, 7 of 7)

- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [Jukebox: A Generative Model for Music](https://mlsanity.com/paper/2005.00341/) (2020)
- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024)
- [Controllable Human-Object Interaction Synthesis](https://mlsanity.com/paper/2312.03913/) (2023)
- [EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generation](https://mlsanity.com/paper/2312.02256/) (2023)
- [Learning to Generate Diverse Dance Motions with Transformer](https://mlsanity.com/paper/2008.08171/) (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)

## Built on by (most cited first, 22 of 22)

- [InterMimic: Towards Universal Whole-Body Control for Physics-Based Human-Object Interactions](https://mlsanity.com/paper/2502.20390/) (2025)
- [DuetGen: Music Driven Two-Person Dance Generation via Hierarchical Masked Modeling](https://mlsanity.com/paper/2506.18680/) (2025)
- [Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis](https://mlsanity.com/paper/2508.02106/) (2025)
- [PRIMAL: Physically Reactive and Interactive Motor Model for Avatar Learning](https://mlsanity.com/paper/2503.17544/) (2025)
- [VolumetricSMPL: A Neural Volumetric Body Model for Efficient Interactions, Contacts, and Collisions](https://mlsanity.com/paper/2506.23236/) (2025)
- [3D Human Interaction Generation: A Survey](https://mlsanity.com/paper/2503.13120/) (2025)
- [ARFlow: Human Action-Reaction Flow Matching with Physical Guidance](https://mlsanity.com/paper/2503.16973/) (2025)
- [Unified Multi-Modal Interactive & Reactive 3D Motion Generation via Rectified Flow](https://mlsanity.com/paper/2509.24099/) (2025)
- [A Plug-and-Play Physical Motion Restoration Approach for In-the-Wild High-Difficulty Motions](https://mlsanity.com/paper/2412.17377/) (2024)
- [Separate to Collaborate: Dual-Stream Diffusion Model for Coordinated Piano Hand Motion Synthesis](https://mlsanity.com/paper/2504.09885/) (2025)
- [PhysiInter: Integrating Physical Mapping for High-Fidelity Human Interaction Generation](https://mlsanity.com/paper/2506.07456/) (2025)
- [MDD: A Dataset for Text-and-Music Conditioned Duet Dance Generation](https://mlsanity.com/paper/2508.16911/) (2025)
- [InteracTalker: Prompt-Based Human-Object Interaction with Co-Speech Gesture Generation](https://mlsanity.com/paper/2512.12664/) (2025)
- [CoMPAS3D: A Dataset and Benchmark for Interactive Motion](https://mlsanity.com/paper/2507.19684/) (2025)
- [Learning to Generate Human-Human-Object Interactions from Textual Descriptions](https://mlsanity.com/paper/2511.20446/) (2025)
- [InfiniteDance: Scalable 3D Dance Generation Towards in-the-wild Generalization](https://mlsanity.com/paper/2603.13375/) (2026)
- [Stability-Driven Motion Generation for Object-Guided Human-Human Co-Manipulation](https://mlsanity.com/paper/2604.20336/) (2026)
- [Social Structure Matters in 3D Human-Human Interaction Generation](https://mlsanity.com/paper/2606.24255/) (2026)
- [Dyads: Artist-Centric, AI-Generated Dance Duets](https://mlsanity.com/paper/2503.03954/) (2025)
- [Generative human motion mimicking through feature extraction in denoising diffusion settings](https://mlsanity.com/paper/2511.00011/) (2025)
- [SalsaAgent: A multimodal embodied language model for interactive dance generation](https://mlsanity.com/paper/2605.29219/) (2026)
- [Inter-X++: A Comprehensive Benchmark for Multimodal Human-Human Interaction Analysis](https://mlsanity.com/paper/2608.20312/) (2026)

## Abstract (arXiv)

Humans perform a variety of interactive motions, among which duet dance is one of the most challenging interactions. However, in terms of human motion generative models, existing works are still unable to generate high-quality interactive motions, especially in the field of duet dance. On the one hand, it is due to the lack of large-scale high-quality datasets. On the other hand, it arises from the incomplete representation of interactive motion and the lack of fine-grained optimization of interactions. To address these challenges, we propose, InterDance, a large-scale duet dance dataset that significantly enhances motion quality, data scale, and the variety of dance genres. Built upon this dataset, we propose a new motion representation that can accurately and comprehensively describe interactive motion. We further introduce a diffusion-based framework with an interaction refinement guidance strategy to optimize the realism of interactions progressively. Extensive experiments demonstrate the effectiveness of our dataset and algorithm.