Multiple Facial Image Editing Using Edge-Aware PDE Learning

Abstract

This paper introduces a novel facial editing tool, called edge-aware mask, to achieve multiple photo-realistic rendering effects in a unified framework. The edge-aware masks facilitate three basic operations for adaptive facial editing, including region selection, edit setting and region blending. Inspired by the state-of-the-art edit propagation and partial differential equation (PDE) learning method, we propose an adaptive PDE model with facial priors for masks generation through edge-aware diffusion. The edge-aware masks can automatically fit the complex region boundary with great accuracy and produce smooth transition between different regions, which significantly improves the visual consistence of face editing and reduce the human intervention. Then, a unified and flexible facial editing framework is constructed, which consists of layer decomposition, edge-aware masks generation, and layer/mask composition. The combinations of multiple facial layers and edge-aware masks can achieve various facial effects simultaneously, including face enhancement, relighting, makeup and face blending etc. Qualitative and quantitative evaluations were performed using different datasets for different facial editing tasks. Experiments demonstrate the effectiveness and flexibility of our methods, and the comparisons with the previous methods indicate that improved results are obtained using the combination of multiple edge-aware masks.