Pose-Transfer

Code for the paper Progressive Pose Attention for Person Image Generation in CVPR19(Oral). The paper is available here.

Video generation with a single image as input. More details can be found in the supplementary materials in our paper.

News

• We have released a new branch PATN_Fine. We introduce a segment-based skip-connection and a novel segment-based style loss, achieving even better results on DeepFashion.
• Video demo is available now. We further improve the performance of our model by introducing a segment-based skip-connection. We will release the code soon. Refer to our supplementary materials for more details.
• Codes for pytorch 1.0 is available now under the branch pytorch_v1.0. The same results on both datasets can be reproduced with the pretrained model.

Notes:

In pytorch 1.0, running_mean and running_var are not saved for the Instance Normalization layer by default. To reproduce our result in the paper, launch python tool/rm_insnorm_running_vars.py to remove corresponding keys in the pretrained model. (Only for the DeepFashion dataset.)

This is Pytorch implementation for pose transfer on both Market1501 and DeepFashion dataset. The code is written by Tengteng Huang and Zhen Zhu.

Requirement

• pytorch(0.3.1)
• torchvision(0.2.0)
• numpy
• scipy
• scikit-image
• pillow
• pandas
• tqdm
• dominate

Getting Started

Installation

• Clone this repo:

git clone https://github.com/tengteng95/Pose-Transfer.git
cd Pose-Transfer

Data Preperation

We provide our dataset split files and extracted keypoints files for convience.

Market1501

• Download the Market-1501 dataset from here. Rename bounding_box_train and bounding_box_test to train and test, and put them under the market_data directory.
• Download train/test splits and train/test key points annotations from Google Drive or Baidu Disk, including market-pairs-train.csv, market-pairs-test.csv, market-annotation-train.csv, market-annotation-train.csv. Put these four files under the market_data directory.
• Generate the pose heatmaps. Launch

python tool/generate_pose_map_market.py

DeepFashion

Note: In our settings, we crop the images of DeepFashion into the resolution of 176x256 in a center-crop manner.

• Download deep fasion dataset in-shop clothes retrival benchmark. You will need to ask a pasword from dataset maintainers.
• Split the raw images into the train split (fashion_data/train) and the test split (fashion_data/test). Crop the images. Launch

python tool/generate_fashion_datasets.py

• Download train/test pairs and train/test key points annotations from Google Drive or Baidu Disk, including fasion-resize-pairs-train.csv, fasion-resize-pairs-test.csv, fasion-resize-annotation-train.csv, fasion-resize-annotation-train.csv. Put these four files under the fashion_data directory.
• Generate the pose heatmaps. Launch

python tool/generate_pose_map_fashion.py

Notes:

Optionally, you can also generate these files by yourself.

1. Keypoints files

We use OpenPose to generate keypoints.

• Download pose estimator from Google Drive or Baidu Disk. Put it under the root folder Pose-Transfer.
• Change the paths input_folder and output_path in tool/compute_coordinates.py. And then launch

python2 compute_coordinates.py

2. Dataset split files

python2 tool/create_pairs_dataset.py

Train a model

Market-1501

python train.py --dataroot ./market_data/ --name market_PATN --model PATN --lambda_GAN 5 --lambda_A 10  --lambda_B 10 --dataset_mode keypoint --no_lsgan --n_layers 3 --norm batch --batchSize 32 --resize_or_crop no --gpu_ids 0 --BP_input_nc 18 --no_flip --which_model_netG PATN --niter 500 --niter_decay 200 --checkpoints_dir ./checkpoints --pairLst ./market_data/market-pairs-train.csv --L1_type l1_plus_perL1 --n_layers_D 3 --with_D_PP 1 --with_D_PB 1  --display_id 0

DeepFashion

python train.py --dataroot ./fashion_data/ --name fashion_PATN --model PATN --lambda_GAN 5 --lambda_A 1 --lambda_B 1 --dataset_mode keypoint --n_layers 3 --norm instance --batchSize 7 --pool_size 0 --resize_or_crop no --gpu_ids 0 --BP_input_nc 18 --no_flip --which_model_netG PATN --niter 500 --niter_decay 200 --checkpoints_dir ./checkpoints --pairLst ./fashion_data/fasion-resize-pairs-train.csv --L1_type l1_plus_perL1 --n_layers_D 3 --with_D_PP 1 --with_D_PB 1  --display_id 0

Test the model

Market1501

python test.py --dataroot ./market_data/ --name market_PATN --model PATN --phase test --dataset_mode keypoint --norm batch --batchSize 1 --resize_or_crop no --gpu_ids 2 --BP_input_nc 18 --no_flip --which_model_netG PATN --checkpoints_dir ./checkpoints --pairLst ./market_data/market-pairs-test.csv --which_epoch latest --results_dir ./results --display_id 0

DeepFashion

python test.py --dataroot ./fashion_data/ --name fashion_PATN --model PATN --phase test --dataset_mode keypoint --norm instance --batchSize 1 --resize_or_crop no --gpu_ids 0 --BP_input_nc 18 --no_flip --which_model_netG PATN --checkpoints_dir ./checkpoints --pairLst ./fashion_data/fasion-resize-pairs-test.csv --which_epoch latest --results_dir ./results --display_id 0

Evaluation

We adopt SSIM, mask-SSIM, IS, mask-IS, DS, and PCKh for evaluation of Market-1501. SSIM, IS, DS, PCKh for DeepFashion.

1) SSIM and mask-SSIM, IS and mask-IS, mask-SSIM

For evaluation, Tensorflow 1.4.1(python3) is required. Please see requirements_tf.txt for details.

For Market-1501:

python tool/getMetrics_market.py

For DeepFashion:

python tool/getMetrics_market.py

If you still have problems for evaluation, please consider using docker.

docker run -v <Pose-Transfer path>:/tmp -w /tmp --runtime=nvidia -it --rm tensorflow/tensorflow:1.4.1-gpu-py3 bash
# now in docker:
$ pip install scikit-image tqdm 
$ python tool/getMetrics_market.py

Refer to this Issue.

2) DS Score

Download pretrained on VOC 300x300 model and install propper caffe version SSD. Put it in the ssd_score forlder.

For Market-1501:

python compute_ssd_score_market.py --input_dir path/to/generated/images

For DeepFashion:

python compute_ssd_score_fashion.py --input_dir path/to/generated/images

3) PCKh

• First, run tool/crop_market.py or tool/crop_fashion.py.
• Download pose estimator from Google Drive or Baidu Disk. Put it under the root folder Pose-Transfer.
• Change the paths input_folder and output_path in tool/compute_coordinates.py. And then launch

python2 compute_coordinates.py

• run tool/calPCKH_fashion.py or tool/calPCKH_market.py

Pre-trained model

Our pre-trained model can be downloaded Google Drive or Baidu Disk.

Citation

If you use this code for your research, please cite our paper.

@inproceedings{zhu2019progressive,
  title={Progressive Pose Attention Transfer for Person Image Generation},
  author={Zhu, Zhen and Huang, Tengteng and Shi, Baoguang and Yu, Miao and Wang, Bofei and Bai, Xiang},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={2347--2356},
  year={2019}
}

Acknowledgments

Our code is based on the popular pytorch-CycleGAN-and-pix2pix.