Implicit Periodic Field Network

This repository contains the code (in PyTorch) for IPFN: Exemplar-based Pattern Synthesis with Implicit Periodic Field Networks (CVPR'2022) by Haiwei Chen, Jiayi Liu, Weikai Chen, Shichen Liu, and Yajie Zhao.

Introduction

IPFN is a fast, lightweighted generative network designed for synthesizing and expanding both stationary and directional visual patterns in both 2D and 3D. Specifically, IPFN maps periodically encoded coordinates and a noise field to image pixels, signed distance, etc. and allows synthesis of visual patterns in an infinitely large space. Below are two animated examples that are synthesized in an expanding space:

Pebble	HoneyComb

arXiv | BibTex

Requirements

The code has been tested on Python3.7, PyTorch 1.7.1 and CUDA (10.1). The additional dependencies can be installed with

python -m pip install -r requirement

Experiments

Datasets We provide several input images, sampled from the Describable Textures Dataset, in the exemplar folder.

Demo We provide a simple walk through of how to train and generate texture images with our model in a demo notebook, where you may either choose to train a synthesizer from scratch or to utilize a pretrained model for synthesizing honeycomb image.

Training Training on a single examplar input can be done with the following command:

# with visdom server
visdom -port 8097 &
python run.py experiment -n EXPERIMENT_NAME dataset -p PATH_TO_IMAGE visdom --port 8097

# without visdom server
python run.py experiment -n EXPERIMENT_NAME dataset -p PATH_TO_IMAGE visdom --display-id -1

In addition, depending on the task of interest, the --input-type arguments may be used to select whether the network is trained in 2D or 3D coordinate space; the --guidance-feature-type may be used to control synthesis of directional patterns.

Contact

Haiwei Chen: chw9308@hotmail.com Any discussions or concerns are welcomed!

Citation If you find our project useful in your research, please consider citing:

@inproceedings{chen2022exemplar,
  title={Exemplar-Based Pattern Synthesis With Implicit Periodic Field Network},
  author={Chen, Haiwei and Liu, Jiayi and Chen, Weikai and Liu, Shichen and Zhao, Yajie},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3708--3717},
  year={2022}
}