Controllable Face Sketch-Photo Synthesis with Flexible Generative Priors

Abstract

Current face sketch-photo synthesis researches generally embrace an image-to-image (I2I) translation pipeline. However, these methods ignore the one-to-many mapping problem (i.e., multiple plausible photo results can correspond to a single input sketch) in sketch-to-photo synthesis task, resulting in significant performance degradation on diverse datasets. Besides, generating high-quality images on limited data is also a challenge for this task. To address these challenges, we propose a dual-path framework that introduces generative priors to better perform cross-domain reconstruction on limited data. The coarse path uses a layer-swapped pre-trained generator to achieve coarse cross-domain reconstruction, and the refinement path further improves the structure and texture details. To align the feature maps between the two paths, we introduce a spatial feature calibration module. Despite this, our framework still struggles to handle diverse datasets. Thanks to the flexibility of generative priors, we can extend the framework to achieve exemplar-guided I2I translation by incorporating an exemplar with style mixing and a proposed semantic-aware style refinement strategy, which addresses the one-to-many mapping problem in sketch-to-photo synthesis task. Furthermore, our framework can perform cross-domain editing by employing off-the-shelf editing methods based on the latent space, achieving fine-grained control. Extensive experiments on diverse datasets demonstrate the superiority of our framework over other state-of-the-art methods.