DanielMengLiu / Lipreading-DenseNet3D

DenseNet3D Model In "LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild", https://arxiv.org/abs/1810.06990

Lipreading-DenseNet3D

DenseNet3D Model In "DenseNet3D Model In "LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild", https://arxiv.org/abs/1810.06990

Introduction

This respository is implementation of the proposed method in LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild. Our paper can be found here.

Dependencies

• Python 3.6.7
• PyTorch 1.0+
• Others

Dataset

This model is pretrained on LRW with RGB lip images(112×112), and then tranfer to LRW-1000 with the same size. We train the model end-to-end.

Training And Testing

You can train or test the model as follow:

python main.py options_lip.toml

Model architecture details and data annotation items are configured in options_lip.toml. Please pay attention that you may need modify the code in options_lip.toml and change the parameters data_root and index_root to make the scripts work just as expected.

Another implmentation: https://github.com/NirHeaven/D3D

Reference

If this repository was useful for your research, please cite our work:

@inproceedings{yang2019lrw,
  title={LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild},
  author={Yang, Shuang and Zhang, Yuanhang and Feng, Dalu and Yang, Mingmin and Wang, Chenhao and Xiao, Jingyun and Long, Keyu and Shan, Shiguang and Chen, Xilin},
  booktitle={2019 14th IEEE International Conference on Automatic Face \& Gesture Recognition (FG 2019)},
  pages={1--8},
  year={2019},
  organization={IEEE},
  url={https://github.com/Fengdalu/Lipreading-DenseNet3D}
}