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 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.
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}
}