LostGANs: Image Synthesis From Reconfigurable Layout and Style

This is implementation of our ICCV19 paper Image Synthesis From Reconfigurable Layout and Style

Network Structure

Installation

Check INSTALL.md for installation instructions.

1. Download pretrained model

Download pretrained models to pretrained_model/

• Pretrained model on COCO
• Pretrained model on VG

2. Train models

python train.py --dataset coco --out_path outputs/

3. Run pretrained model

python test.py --dataset coco --model_path pretrained_model/G_coco.pth --sample_path samples/coco/

Results

Multiple samples generated from same layout

Generation results by adding new objects or change spatial position of object

Linear interpolation of instance style

Synthesized images and learned masks for given layout

Contact

Please feel free to report issues and any related problems to Wei Sun (wsun12 at ncsu dot edu) and Tianfu Wu (tianfu_wu at ncsu dot edu).

Reference

• Synchronized-BatchNorm-PyTorch: https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
• Image Generation from Scene Graphs: https://github.com/google/sg2im
• Faster R-CNN and Mask R-CNN in PyTorch 1.0: https://github.com/facebookresearch/maskrcnn-benchmark