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Neural-Texture-Extraction-Distribution

The PyTorch implementation for our paper "Neural Texture Extraction and Distribution for Controllable Person Image Synthesis" (CVPR2022 Oral)

We propose a Neural-Texture-Extraction-Distribution operation for controllable person image synthesis. Our model can be used to control the pose and appearance of a reference image:

• Pose Control

• Appearance Control

News

• 2022.6.25 Web demo available through Replicate:

  :rocket: Demo and Docker image on Replicate

• 2022.4.30 Colab demos are provided for quick exploration.

• 2022.4.28 Code for PyTorch is available now!

Installation

Requirements

• Python 3
• PyTorch 1.7.1
• CUDA 10.2

Conda Installation

# 1. Create a conda virtual environment.
conda create -n NTED python=3.6
conda activate NTED
conda install -c pytorch pytorch=1.7.1 torchvision cudatoolkit=10.2

# 2. Clone the Repo and Install dependencies
git clone --recursive https://github.com/RenYurui/Neural-Texture-Extraction-Distribution.git
pip install -r requirements.txt

# 3. Install mmfashion (for appearance control only)
pip install mmcv==0.5.1
pip install pycocotools==2.0.4
cd ./scripts
chmod +x insert_mmfashion2mmdetection.sh
./insert_mmfashion2mmdetection.sh
cd ../third_part/mmdetection
pip install -v -e .

Demo

Several demos are provided. Please first download the resources by runing

cd scripts
./download_demos.sh

Pose Transfer

Run the following code for the results.

PATH_TO_OUTPUT=./demo_results
python demo.py \
--config ./config/fashion_512.yaml \
--which_iter 495400 \
--name fashion_512 \
--file_pairs ./txt_files/demo.txt \
--input_dir ./demo_images \
--output_dir $PATH_TO_OUTPUT

Appearance Control

Meanwhile, run the following code for the appearance control demo.

python appearance_control.py \
--config ./config/fashion_512.yaml \
--name fashion_512 \
--which_iter 495400 \
--input_dir ./demo_images \
--file_pairs ./txt_files/appearance_control.txt

Colab Demo

Please check the Colab Demos for pose control and appearance control.

Dataset

• 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 rename the obtained folder as img and put it under the ./dataset/deepfashion directory.

• We split the train/test set following GFLA. Several images with significant occlusions are removed from the training set. Download the train/test pairs and the keypoints pose.zip extracted with Openpose by runing:

  cd scripts
  ./download_dataset.sh
  

  Or you can download these files manually：

  • Download the train/test pairs from Google Drive including train_pairs.txt, test_pairs.txt, train.lst, test.lst. Put these files under the ./dataset/deepfashion directory.
  • Download the keypoints pose.rar extracted with Openpose from Google Driven. Unzip and put the obtained floder under the ./dataset/deepfashion directory.

• Run the following code to save images to lmdb dataset.

  python -m scripts.prepare_data \
  --root ./dataset/deepfashion \
  --out ./dataset/deepfashion
  

Training

This project supports multi-GPUs training. The following code shows an example for training the model with 512x352 images using 4 GPUs.

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch \
--nproc_per_node=4 \
--master_port 1234 train.py \
--config ./config/fashion_512.yaml \
--name $name_of_your_experiment

All configs for this experiment are saved in ./config/fashion_512.yaml. If you change the number of GPUs, you may need to modify the batch_size in ./config/fashion_512.yaml to ensure using a same batch_size.

Inference

• Download the trained weights for 512x352 images and 256x176 images. Put the obtained checkpoints under ./result/fashion_512 and ./result/fashion_256 respectively.

• Run the following code to evaluate the trained model:

  # run evaluation for 512x352 images
  python -m torch.distributed.launch \
  --nproc_per_node=1 \
  --master_port 12345 inference.py \
  --config ./config/fashion_512.yaml \
  --name fashion_512 \
  --no_resume \
    --which_iter 495400 \
  --output_dir ./result/fashion_512/inference 
  
  # run evaluation for 256x176 images
  python -m torch.distributed.launch \
  --nproc_per_node=1 \
  --master_port 12345 inference.py \
  --config ./config/fashion_256.yaml \
  --name fashion_256 \
  --no_resume \
  --which_iter 495400 \
  --output_dir ./result/fashion_256/inference 
  

The result images are save in ./result/fashion_512/inference and ./result/fashion_256/inference.