Unsupervised Scene Sketch to Photo Synthesis

• URL: http://arxiv.org/abs/2209.02834v1
• Date: Tue, 6 Sep 2022 22:25:06 GMT
• Title: Unsupervised Scene Sketch to Photo Synthesis
• Authors: Jiayun Wang, Sangryul Jeon, Stella X. Yu, Xi Zhang, Himanshu Arora, Yu Lou
• Abstract summary: We present a method for synthesizing realistic photos from scene sketches. Our framework learns from readily available large-scale photo datasets in an unsupervised manner. We also demonstrate that our framework facilitates a controllable manipulation of photo synthesis by editing strokes of corresponding sketches.
• Score: 40.044690369936184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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