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PERSON_IMAGE_GENERATION.md

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Pose-Guided Person Image Generation

The Training and Testing details of the pose-guided person image generation task are provided here.

Dataset

Market1501

  • Download the Market-1501 dataset from here. Rename bounding_box_train and bounding_box_test as train and test, and put them under the ./dataset/market directory
  • Download train/test key points annotations from Google Drive including market-pairs-train.csv, market-pairs-test.csv, market-annotation-train.csv, market-annotation-train.csv. Put these files under the ./dataset/market directory.

DeepFashion

  • Download img_highres.zip of the DeepFashion Dataset from In-shop Clothes Retrieval Benchmark.

  • Unzip img_highres.zip. You will need to ask for password from the dataset maintainers. Then put the obtained folder img_highres under the ./dataset/fashion directory.

  • Download train/test key points annotations and the dataset list from Google Drive including fashion-pairs-train.csv, fashion-pairs-test.csv, fashion-annotation-train.csv, fashion-annotation-train.csv, train.lst, test.lst. Put these files under the ./dataset/fashion directory.

  • Run the following code to split the train/test dataset.

    python script/generate_fashion_datasets.py

Evaluation

Download the trained weights from Fashion, Market. Put the obtained checkpoints under ./result/pose_fashion_checkpoints and ./result/pose_market_checkpoints respectively.

Run the following codes to obtain the pose-based transformation results.

# Test the DeepFashion dataset 
python test.py \
--name=pose_fashion_checkpoints \
--model=pose \
--attn_layer=2,3 \
--kernel_size=2=5,3=3 \
--gpu_id=0 \
--dataset_mode=fashion \
--dataroot=./dataset/fashion \
--results_dir=./eval_results/fashion

# Test the market dataset
python test.py \
--name=pose_market_checkpoints \
--model=pose \
--attn_layer=2 \
--kernel_size=2=3 \
--gpu_id=0 \
--dataset_mode=market \
--dataroot=./dataset/market \
--results_dir=./eval_results/market

You can use the provided evaluation codes to evaluate the performance of our models.

# evaluate the performance (FID and LPIPS scores) over the DeepFashion dataset.
CUDA_VISIBLE_DEVICES=0 python -m  script.metrics \
--gt_path=./dataset/fashion/test_256 \
--distorated_path=./eval_results/fashion \
--fid_real_path=./dataset/fashion/train_256 \
--name=./fashion

# evaluate the performance (FID and LPIPS scores) over the Market dataset.
CUDA_VISIBLE_DEVICES=0 python -m  script.metrics \
--gt_path=./dataset/market/test_12864 \
--distorated_path=./eval_results/market \
--fid_real_path=./dataset/market/train_12864 \
--name=./market_12864

Note:

  • We calculate the LPIPS scores using the code provided by the official repository PerceptualSimilarity. Please clone their repository and put the folder PerceptualSimilarity to the folder script.
  • For FID, the real data distributions are calculated over the whole training set.

Training

We train our model in stages. The Flow Field Estimator is first trained to generate flow fields. Then we train the whole model in an end-to-end manner.

For example, If you want to train our model with the DeepFashion dataset. You can use the following code.

# First train the Flow Field Estimator.
python train.py \
--name=fashion \
--model=poseflownet \
--attn_layer=2,3 \
--kernel_size=2=5,3=3 \
--gpu_id=0 \
--dataset_mode=fashion \
--dataroot=./dataset/fashion 

# Then, train the whole model in an end-to-end manner.
python train.py \
--name=fashion \
--model=pose \
--attn_layer=2,3 \
--kernel_size=2=5,3=3 \
--gpu_id=0 \
--dataset_mode=fashion \
--dataroot=./dataset/fashion \
--continue_train

The visdom is required to show the temporary results. You can access these results with:

http://localhost:8096