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

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.

• python 3.6.7
• pytorch 1.0.0.dev20181103
• Others

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.

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

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

@article{shuang18LRW1000,
  title={LRW-1000: A Naturally-Distributed Large-Scale Benchmark for Lip Reading in the Wild},
  author={Shuang Yang, Yuanhang Zhang, Dalu Feng, Mingmin Yang, Chenhao Wang, Jingyun Xiao, Keyu Long, Shiguang Shan, Xilin Chen},
  booktitle={arXiv},
  year={2018}
}