Download following pre-processed training data (10GB) and unzip into ./data/300W_LP/
Filelist Images Textures Masks
Download following 3DMM definition and unzip into current folder (./) 3DMM_definition.zip
Please edit TF_newop/compile_op_v2_sz224.sh based on your TF version and whether you install TF with Anaconda (instruction in the file)
$ # Compile
$ cd TF_newop/
$ ./compile_op_v2_sz224.sh
$ # Run an example
$ python rendering_example.py
Currently the code is working but not optimal (i.e see line 139 of TF_newop/cuda_op_kernel_v2_sz224.cu.cc) also the image size is hard-coded. Any contribution is welcome!
Note: In recent TF version, set --is_
Pretraining
python main_non_linear_3DMM.py --batch_size 128 --sample_size 128 --is_train True --learning_rate 0.001 --ouput_size 224 \
--gf_dim 32 --df_dim 32 --dfc_dim 320 --gfc_dim 320 --z_dim 20 --c_dim 3 \
--is_using_landmark True --shape_loss l2 --tex_loss l1 \
--is_using_recon False --is_using_frecon False --is_partbase_albedo False --is_using_symetry True \
--is_albedo_supervision False --is_batchwise_white_shading True --is_const_albedo True --is_const_local_albedo False --is_smoothness True
--gpu 0,1,2,3
Finetunning Manually reduce the m_loss, shape_loss weight by 10 times
python main_non_linear_3DMM.py --batch_size 64 --sample_size 64 --is_train True --learning_rate 0.001 --ouput_size 224 \
--gf_dim 32 --df_dim 32 --dfc_dim 320 --gfc_dim 320 --z_dim 20 --c_dim 3 \
--is_using_landmark True --shape_loss l2 --tex_loss l1 \
--is_using_recon True --is_using_frecon True --is_partbase_albedo False --is_using_symetry True \
--is_albedo_supervision False --is_batchwise_white_shading True --is_const_albedo True --is_const_local_albedo True --is_smoothness True
--gpu 0,1,2,3 \
This is the pretrained model of CVPR'19 paper. Input images are 256 x 256.
If you find this work useful, please cite our papers with the following bibtex:
@inproceedings{ tran2019towards,
author = { Luan Tran and Feng Liu and Xiaoming Liu },
title = { Towards High-fidelity Nonlinear 3D Face Morphable Model },
booktitle = { In Proceeding of IEEE Computer Vision and Pattern Recognition },
address = { Long Beach, CA },
month = { June },
year = { 2019 },
}
@article{ tran2018on,
author = { Luan Tran and Xiaoming Liu },
title = { On Learning 3D Face Morphable Model from In-the-wild Images },
journal = { IEEE Transactions on Pattern Analysis and Machine Intelligence },
month = { July },
year = { 2019 },
}
@inproceedings{ tran2018nonlinear,
author = { Luan Tran and Xiaoming Liu },
title = { Nonlinear 3D Face Morphable Model },
booktitle = { IEEE Computer Vision and Pattern Recognition (CVPR) },
address = { Salt Lake City, UT },
month = { June },
year = { 2018 },
}
If you have any questions, feel free to drop an email to tranluan@msu.edu.