This is a pytorch implementation of the CVPR 2019 paper Dense Intrinsic Appearance Flow for Human Pose Transfer.
- python 2.7
- pytorch (0.4.0)
- numpy
- opencv
- scikit-image
- tqdm
- imageio
Install dependencies:
pip install -r requirements.txt
Download and unzip preprocessed datasets with the following scripts.
bash scripts/download_deepfashion.sh
bash scripts/download_market1501.sh
Or you can manually download them from the following links:
- DeepFashion (23GB): Google Drive
- Market-1501 (9GB): Google Drive
Download pretrained models with the following scripts.
bash scripts/download_models.sh
Pretrained models below will be downloaded into the folder ./checkpoints. You can manually donwload them from here.
| Deepfashion | Market-1501 | Others |
|---|---|---|
|
|
|
- Run scripts/test_pose_transfer.py to generate images and compute SSIM score.
python scripts/test_pose_transfer.py --gpu_ids 0 --id PoseTransfer_0.5 --which_epoch best --save_output
- Compute inception score with the following script. (Note that this script is derived from improved-gan and needs Tensorflow)
# python scripts/inception_score.py image_dir gpu_ids
python scripts/inception_score.py checkpoints/PoseTransfer_0.5/output/ 0
- Compute fashionIS and AttrRec-k with the following scripts.
# FashionIS
python scripts/fashion_inception_score.py --test_dir checkpoints/PoseTransfer_0.5/output/
# AttrRec-k
python scripts/fashion_attribute_score.py --test_dir checkpoints/PoseTransfer_0.5/output/
- Run scripts/test_pose_transfer.py to generate images and compute SSIM/masked-SSIM score.
python scripts/test_pose_transfer.py --gpu_ids 0 --id PoseTransfer_m0.5 --which_epoch best --save_output --masked
- Compute inception score or masked inception score with following scripts.
# IS
python scripts/inception_score.py checkpoints/PoseTransfer_m0.5/output/ 0
# masked-IS (only for market-1501)
python scripts/masked_inception_score.py checkpoints/PoseTransfer_m0.5/output/ 0
- Train flow regression module. (See all options in ./options/flow_regression_options.py)
python scripts/train_flow_regression_module.py --id id_flow --gpu_ids 0 --which_model unet --dataset_name deepfashion
You can alternativelly set --which_model unet_v2 to use a improved version of network architecture with fewer parameters (only tested on Market-1501).
- Train human pose transfer models. Set
--pretrained_flow_idand--pretrained_flow_epochto load the flow regression module. (See all options in ./options/pose_transfer_options.py)
# w/o. dual encoder
python scripts/train_pose_transfer_model.py --id id_pose_1 --gpu_ids 1 --dataset_name deepfashion --which_model_G unet
# w/o. flow
python scripts/train_pose_transfer_model.py --id id_pose_2 --gpu_ids 2 --dataset_name deepfashion --which_model_G dual_unet --G_feat_warp 0
# w/o. visibility
python scripts/train_pose_transfer_model.py --id id_pose_3 --gpu_ids 3 --dataset_name deepfashion --which_model_G dual_unet --G_feat_warp 1 --G_vis_mode none
# w/o. pixel warping
python scripts/train_pose_transfer_model.py --id id_pose_4 --gpu_ids 4 --dataset_name deepfashion --which_model_G dual_unet --G_feat_warp 1 --G_vis_mode residual
# full (need a pretrained pose transfer model without pixel warping)
python scripts/train_pose_transfer_model.py --id id_pose_5 --gpu_ids 5 --dataset_name deepfashion --which_model_G dual_unet --pretrained_G_id id_pose_4 --pretrained_G_epoch 8
Set --dataset_name market to train models on Market-1501 dataset. Data related parameters will be automatically adjusted (see .auto_set() in ./options/flow_regression_options.py and ./options/pose_transfer_options.py for details).
@inproceedings{li2019dense,
author = {Li, Yining and Huang, Chen and Loy, Chen Change},
title = {Dense Intrinsic Appearance Flow for Human Pose Transfer},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
year = {2019}}
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