Listen, Denoise, Action!

This repository provides code and models for the paper Listen, denoise, action! Audio-driven motion synthesis with diffusion models.

Please watch the following video for an introduction to our work:

• SIGGRAPH 2023 presentation

For video samples and a general overview, please see our project page. For the new dance dataset with high-quality mocap, please see the Motorica Dance Dataset.

Installation

We provide a Docker file and requirements.txt for installation using a Docker image or Conda.

Installation using Conda

conda install python=3.9
conda install -c conda-forge mpi4py mpich
pip install -r requirements.txt

Dance synthesis demo

Data and pretrained models

Please download our pretrained dance models here and move them to the pretrained_models folder. We include processed music inputs from the test dataset in the data folder for generating dances from the model.

Synthesis scripts

You can use the following shell scripts for reproducing the dance user studies in the paper:

./experiments/dance_LDA.sh
./experiments/dance_LDA-U.sh

To try out locomotion synthesis, please go to https://www.motorica.ai/.

Training data

The four main training datasets from our SIGGRAPH 2023 paper are available online:

• The Trinity Speech Gesture Dataset
• The ZEGGS dataset
• The 100STYLE dataset
• The Motorica Dance Dataset, a new dataset with high-quality dance mocap released together with our paper

License and copyright information

The contents of this repository may not be used for any purpose other than academic research. It is free to use for research purposes by academic institutes, companies, and individuals. Use for commercial purposes is not permitted without prior written consent from Motorica AB. If you are interested in using the codebase, pretrained models or the dataset for commercial purposes or non-research purposes, please contact us at info@motorica.ai in advance. Unauthorised redistribution is prohibited without written approval.

Attribution

Please include the following citations in any preprints and publications that use this repository.

@article{alexanderson2023listen,
  title={Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models},
  author={Alexanderson, Simon and Nagy, Rajmund and Beskow, Jonas and Henter, Gustav Eje},
  year={2023}
  issue_date={August 2023},
  publisher={ACM},
  volume={42},
  number={4},
  doi={10.1145/3592458},
  journal={ACM Trans. Graph.},
  articleno={44},
  numpages={20},
  pages={44:1--44:20}
}

The code for translation-invariant self-attention (TISA) was written by Ulme Wennberg. Please cite the correspoding ACL 2021 article if you use this code.