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LORIS

This is the official implementation of "Long-Term Rhythmic Video Soundtracker", ICML2023.

Jiashuo Yu, Yaohui Wang, Xinyuan Chen, Xiao Sun, and Yu Qiao.

OpenGVLab, Shanghai Artificial Intelligence Laboratory

Arxiv | Project Page

Introduction

We present Long-Term Rhythmic Video Soundtracker (LORIS), a novel framework to synthesize long-term conditional waveforms in sync with visual cues. Our framework consists of a latent conditional diffusion probabilistic model to perform waveform synthesis. Furthermore, a series of context-aware conditioning encoders are proposed to take temporal information into consideration for a long-term generation. We also extend our model's applicability from dances to multiple sports scenarios such as floor exercise and figure skating. To perform comprehensive evaluations, we establish a benchmark for rhythmic video soundtracks including the pre-processed dataset, improved evaluation metrics, and robust generative baselines.

How to Start

pip install -r requirements.txt

Training

bash scripts/loris_{subset}_s{length}.sh

Inference

bash scripts/infer_{subset}_s{length}.sh

Dataset

Dataset is available in huggingface.

from datasets import load_dataset
dataset = load_dataset("OpenGVLab/LORIS")

Model Zoo

We provide the pre-trained checkpoints and backbone audio diffusion model in huggingface.

It should be noted that these checkpoints must only be used for research purposes.

Citation

@inproceedings{Yu2023Long,
title={Long-Term Rhythmic Video Soundtracker},
author={Yu, Jiashuo and Wang, Yaohui and Chen, Xinyuan and Sun, Xiao and Qiao, Yu },
booktitle={International Conference on Machine Learning (ICML)},
year={2023}
}

Acknowledgement

We would like to thank the authors of previous related projects for generously sharing their code and insights: audio-diffusion-pytorch, CDCD, D2M-GAN, VQ-Diffusion, and JukeBox.