# Taming Diffusion Models for Music-driven Conducting Motion Generation

arXiv: https://arxiv.org/abs/2306.10065 · HTML: https://mlsanity.com/paper/2306.10065/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 18 of its 21 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation](https://mlsanity.com/paper/2303.09119/) (2023)
- [A Survey on Deep Learning for Skeleton-Based Human Animation](https://mlsanity.com/paper/2110.06901/) (2021)
- [Temporally Guided Music-to-Body-Movement Generation](https://mlsanity.com/paper/2009.08015/) (2020)
- [VirtualConductor: Music-driven Conducting Video Generation System](https://mlsanity.com/paper/2108.04350/) (2021)

## Built on by (most cited first, 7 of 7)

- [Towards Open Domain Text-Driven Synthesis of Multi-Person Motions](https://mlsanity.com/paper/2405.18483/) (2024)
- [Integrating View Conditions for Image Synthesis](https://mlsanity.com/paper/2310.16002/) (2023)
- [SocialGen: Modeling Multi-Human Social Interaction with Language Models](https://mlsanity.com/paper/2503.22906/) (2025)
- [BOTH2Hands: Inferring 3D Hands from Both Text Prompts and Body Dynamics](https://mlsanity.com/paper/2312.07937/) (2023)
- [T3M: Text Guided 3D Human Motion Synthesis from Speech](https://mlsanity.com/paper/2408.12885/) (2024)
- [SpeechAct: Towards Generating Whole-body Motion from Speech](https://mlsanity.com/paper/2311.17425/) (2023)
- [BiTDiff: Fine-Grained 3D Conducting Motion Generation via BiMamba-Transformer Diffusion](https://mlsanity.com/paper/2604.04395/) (2026)

## Abstract (arXiv)

Generating the motion of orchestral conductors from a given piece of symphony music is a challenging task since it requires a model to learn semantic music features and capture the underlying distribution of real conducting motion. Prior works have applied Generative Adversarial Networks (GAN) to this task, but the promising diffusion model, which recently showed its advantages in terms of both training stability and output quality, has not been exploited in this context. This paper presents Diffusion-Conductor, a novel DDIM-based approach for music-driven conducting motion generation, which integrates the diffusion model to a two-stage learning framework. We further propose a random masking strategy to improve the feature robustness, and use a pair of geometric loss functions to impose additional regularizations and increase motion diversity. We also design several novel metrics, including Frechet Gesture Distance (FGD) and Beat Consistency Score (BC) for a more comprehensive evaluation of the generated motion. Experimental results demonstrate the advantages of our model.