Our image processing code is largely adapted from hongsukchoi/3DCrowdNet_RELEASE.
Installation
conda create -n 3dportraitgan_data python=3.8
activate 3dportraitgan_data
cd data_processing
pip install torch==1.10.2+cu113 torchvision==0.11.3+cu113 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt
python -m pip install -e detectron2
For windows:
pip install pywin32==306
For windows users who experience errors during detectron2 installation, please open a x64 Native Tools Command Prompt for Visual Studio and execute python -m pip install -e detectron2.
Pretrained models
| Download Link | Save Path |
|---|---|
| R_101_FPN_DL_soft_s1x.pkl | ./data_processing/detectron2/projects/DensePose |
| phi_smpl_27554_256.pkl | ./data_processing/detectron2/projects/DensePose |
| pose_higher_hrnet_w32_512.pth | ./data_processing/HigherHRNet-Human-Pose-Estimation/models/pytorch/pose_coco |
| crowdhuman_yolov5m.pt | ./data_processing/yolov5_crowdhuman |
| basicModel_neutral_lbs_10_207_0_v1.0.0.pkl | ./data_processing/common/utils/smplpytorch/smplpytorch/native/models |
| VPOSER_CKPT | ./data_processing/common/utils/human_model_files/smpl/VPOSER_CKPT |
| J_regressor_extra.npy | ./data_processing/data |
| demo_checkpoint.pth.tar | ./data_processing/demo |
If you encounter RuntimeError: Subtraction, the - operator, with a bool tensor is not supported., you may refer to this issue for a solution or change L301~L304 of anaconda3/lib/python3.8/site-packages/torchgeometry/core/conversion.py to below:
mask_c0 = mask_d2.float() * mask_d0_d1.float()
mask_c1 = mask_d2.float() * (1 - mask_d0_d1.float())
mask_c2 = (1 - mask_d2.float()) * mask_d0_nd1.float()
mask_c3 = (1 - mask_d2.float()) * (1 - mask_d0_nd1.float())
Put all real images in $TEST_DATA_DIR$/samples
Then process the randomly generated images to produce aligned images following the alignment setting of 3DPortraitGAN:
activate 3dportraitgan_data
python preprocess_img.py --test_data_dir=$TEST_DATA_DIR$
Results will be saved to $TEST_DATA_DIR$/samples_new_crop.