# Generative Choreography using Deep Learning

arXiv: https://arxiv.org/abs/1605.06921 · HTML: https://mlsanity.com/paper/1605.06921/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2016.

## Built on by (most cited first, 12 of 12)

- [Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models](https://mlsanity.com/paper/2211.09707/) (2022)
- [Designing Creative AI Partners with COFI: A Framework for Modeling Interaction in Human-AI Co-Creative Systems](https://mlsanity.com/paper/2204.07666/) (2022)
- [Transflower: probabilistic autoregressive dance generation with multimodal attention](https://mlsanity.com/paper/2106.13871/) (2021)
- [Dance with You: The Diversity Controllable Dancer Generation via Diffusion Models](https://mlsanity.com/paper/2308.13551/) (2023)
- [Controllable Motion Synthesis and Reconstruction with Autoregressive Diffusion Models](https://mlsanity.com/paper/2304.04681/) (2023)
- [DanceCamAnimator: Keyframe-Based Controllable 3D Dance Camera Synthesis](https://mlsanity.com/paper/2409.14925/) (2024)
- [Dance Generation by Sound Symbolic Words](https://mlsanity.com/paper/2306.03646/) (2023)
- [Adversarial Attention for Human Motion Synthesis](https://mlsanity.com/paper/2204.11751/) (2022)
- [Learning Human-Inspired Force Strategies for Robotic Assembly](https://mlsanity.com/paper/2303.12440/) (2023)
- [Dyads: Artist-Centric, AI-Generated Dance Duets](https://mlsanity.com/paper/2503.03954/) (2025)
- [Invisible Strings: Revealing Latent Dancer-to-Dancer Interactions with Graph Neural Networks](https://mlsanity.com/paper/2503.04816/) (2025)
- [Generative human motion mimicking through feature extraction in denoising diffusion settings](https://mlsanity.com/paper/2511.00011/) (2025)

## Abstract (arXiv)

Recent advances in deep learning have enabled the extraction of high-level features from raw sensor data which has opened up new possibilities in many different fields, including computer generated choreography. In this paper we present a system chor-rnn for generating novel choreographic material in the nuanced choreographic language and style of an individual choreographer. It also shows promising results in producing a higher level compositional cohesion, rather than just generating sequences of movement. At the core of chor-rnn is a deep recurrent neural network trained on raw motion capture data and that can generate new dance sequences for a solo dancer. Chor-rnn can be used for collaborative human-machine choreography or as a creative catalyst, serving as inspiration for a choreographer.