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Single Stage Virtual Try-on via Deformable Attention Flows

Official code for ECCV2022 paper "Single Stage Virtual Try-on via Deformable Attention Flows"

We propose a single-stage try-on framework by developing a novel Deformable Attention Flow (DAFlow), which applies the deformable attention scheme to multi-flow estimation. With pose keypoints as the guidance only, the self- and cross-deformable attention flows are estimated for the reference person and the garment images, respectively. By sampling multiple flow fields, the feature-level and pixel-level information from different semantic areas are simultaneously extracted and merged through the attention mechanism. It enables clothes warping and body synthesizing at the same time which leads to photo-realistic results in an end-to-end manner.

Prepare

• Datasets: Train_data Test_data, put the folders into data/VITON
• Checkpoints: ckpt_viton.pt
• python 3.6
• pytorch1.7
• torchvision 0.8

The data process scripts in data/data_process, the original data is from CPVTON-plus. Unzip the viton_plus.zip from CPVTON-plus.

Drawing img_agnostic: python get_img_agnostic.py --data_path img_path --data_txt data.txt

Drawing skeleton img (json file as the openpose format): python draw_keypoints.py --json_path json_files_path --save_path save_path

Inference

Paired setting: In the paired setting, we have ground truth images.

python -u test_SDAFNet_viton.py -b 8 --name TEST_PAIR --mode test --dataset_list VITON/test_pairs.txt

Unpaired setting: In the unpaired setting, we do not have ground truth images. The test_unpairs.txt is same with CPVTON-plus and PFAFN

python -u test_SDAFNet_viton.py -b 8 --name TEST_UNPAIR --mode test --dataset_list VITON/test_unpairs.txt

Train

python train_SDAFNet_viton.py -b 8 --name VITON

Evaluation

• VITON dataset

Download the checkpoint, which gets higher scores than reported in paper.

Paired setting. SSIM: 0.8539 using the pytorch SSIM repo. https://github.com/Po-Hsun-Su/pytorch-ssim

UnPaired setting. FID: 10.55 using the implementation to evaluate. https://github.com/toshas/torch-fidelity

Results

• VITON dataset

• MPV dataset

• FashionVideo

• ShapeNet

Acknowledgement

Our code references the implementation of ClotFlow and PFAPN, including the feature extractors, feature pyramid networks (FPN) , and the design of the cascaded structure. Thanks for their awesome works.

License

The use of this code is RESTRICTED to non-commercial research and educational purposes.