The source code for our paper "Learning Semantic Person Image Generation by Region-Adaptive Normalization" (CVPR 2021)
Clone this repo
git clone https://github.com/cszy98/SPGNet.git. cd SPGNet Prerequisites
- python3.7 - pytorch1.2.0 + torchvision0.4.0 - numpy - opencv - tqdm Create environment and install dependencies:
# 1. Create a conda virtual environment. conda create -n spgnet python=3.7 anaconda source activate spgnet # 2. Install dependency pip install -r requirements.txt The DeepFashion and Market-1501 datasets can be downloaded from GoogleDrive.
The pretrained models can be downloaded from GoogleDrive.
Test on DeepFashion
python scripts/test_pose_transfer_model.py --id deepfashion --gpu_ids 0 --dataset_name deepfashion --which_model_G dual_unet --G_feat_warp 1 --G_vis_mode residual --pretrained_flow_id FlowReg_deepfashion --pretrained_flow_epoch best --dataset_type pose_transfer_parsing --which_epoch latest --batch_size 4 --save_output --output_dir output Test on Market-1501
python scripts/test_pose_transfer_model.py --id market --gpu_ids 0 --dataset_name market --which_model_G dual_unet --G_feat_warp 1 --G_vis_mode residual --pretrained_flow_id FlowReg_market --pretrained_flow_epoch best --dataset_type pose_transfer_parsing_market --which_epoch latest --batch_size 1 --save_output --output_dir output Evaluate
Run eval_deepfashion.sh and eval_market.sh to evaluate LPIPS and FID on DeepFashion and Market-1501, respectively.
To evaluate the PCKh, download pose estimator from GoogleDrive and put it under the root folder. Then change the path in tool/compute_coordinates.py and launch python2 compute_coordinates.py. After that, launch python tool/calPCKH_market.py or python tool/calPCKH_fashion.py to get PCKh. Please refer to Pose-Transfer for more details.
1. Train on DeepFashion
python scripts/train_pose_transfer_model.py --id deepfashion --gpu_ids 0,1 --dataset_name deepfashion --which_model_G dual_unet --G_feat_warp 1 --G_vis_mode residual --pretrained_flow_id FlowReg_deepfashion --pretrained_flow_epoch best --dataset_type pose_transfer_parsing --check_grad_freq 3000 --batch_size 4 --n_epoch 45 2. Train on Market-1501
python scripts/train_pose_transfer_model.py --id marketest --gpu_ids 0,1,2,3 --dataset_name market --which_model_G dual_unet --G_feat_warp 1 --G_vis_mode residual --pretrained_flow_id FlowReg_market --pretrained_flow_epoch best --dataset_type pose_transfer_parsing_market --check_grad_freq 3000 --batch_size 32 --n_epoch 10 If you find our work useful in your research or publication, please cite:
@article{lv2021learning, title={Learning Semantic Person Image Generation by Region-Adaptive Normalization}, author={Lv, Zhengyao and Li, Xiaoming and Li, Xin and Li, Fu and Lin, Tianwei and He, Dongliang and Zuo, Wangmeng}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}, year = {2021} } This code borrows heavily from intrinsic_flow and Pose-Transfer.