Unsupervised Scene Sketch to Photo Synthesis

Jiayun Wang      Sangryul Jeon      Stella X. Yu      Xi Zhang      Himanshu Arora      Yu Lou     
ECCVW 2022


Sketches make an intuitive and powerful visual expression as they are fast executed freehand drawings. We present a method for synthesizing realistic photos from scene sketches. Without the need for sketch and photo pairs, our framework directly learns from readily available large-scale photo datasets in an unsupervised manner. To this end, we introduce a standardization module that provides pseudo sketch-photo pairs during training by converting photos and sketches to a standardized domain, i.e. the edge map. The reduced domain gap between sketch and photo also allows us to disentangle them into two components: holistic scene structures and low-level visual styles such as color and texture. Taking this advantage, we synthesize a photo-realistic image by combining the structure of a sketch and the visual style of a reference photo. Extensive experimental results on perceptual similarity metrics and human perceptual studies show the proposed method could generate realistic photos with high fidelity from scene sketches and outperform state-of-the-art photo synthesis baselines. We also demonstrate that our framework facilitates a controllable manipulation of photo synthesis by editing strokes of corresponding sketches, delivering more fine-grained details than previous approaches that rely on region-level editing.

Public Video



  title={Unsupervised Scene Sketch to Photo Synthesis},
  author={Wang, Jiayun and Jeon, Sangryul and Yu, Stella X and Zhang, Xi and Arora, Himanshu and Lou, Yu},
  journal={arXiv preprint arXiv:2209.02834},