Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches

• URL: http://arxiv.org/abs/2001.02890v1
• Date: Thu, 9 Jan 2020 08:57:50 GMT
• Title: Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches
• Authors: Shuai Yang, Zhangyang Wang, Jiaying Liu, Zongming Guo
• Abstract summary: Sketch-based image editing aims to synthesize and modify photos based on the structural information provided by the human-drawn sketches. Deep Plastic Surgery is a novel, robust and controllable image editing framework that allows users to interactively edit images using hand-drawn sketch inputs.
• Score: 133.01690754567252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sketch-based image editing aims to synthesize and modify photos based on the structural information provided by the human-drawn sketches. Since sketches are difficult to collect, previous methods mainly use edge maps instead of sketches to train models (referred to as edge-based models). However, sketches display great structural discrepancy with edge maps, thus failing edge-based models. Moreover, sketches often demonstrate huge variety among different users, demanding even higher generalizability and robustness for the editing model to work. In this paper, we propose Deep Plastic Surgery, a novel, robust and controllable image editing framework that allows users to interactively edit images using hand-drawn sketch inputs. We present a sketch refinement strategy, as inspired by the coarse-to-fine drawing process of the artists, which we show can help our model well adapt to casual and varied sketches without the need for real sketch training data. Our model further provides a refinement level control parameter that enables users to flexibly define how "reliable" the input sketch should be considered for the final output, balancing between sketch faithfulness and output verisimilitude (as the two goals might contradict if the input sketch is drawn poorly). To achieve the multi-level refinement, we introduce a style-based module for level conditioning, which allows adaptive feature representations for different levels in a singe network. Extensive experimental results demonstrate the superiority of our approach in improving the visual quality and user controllablity of image editing over the state-of-the-art methods.

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