基于层次对比生成对抗网络的非配对素描人脸合成，首发论文 -凯发网

unpaired sketch face synthesis based on hierarchical contrast generative adversarial network

• 1、school of information and communication engineering, beijing information science and technology university, beijing 100029, china

abstract：current facial sketch synthesis methods suffer from excessive reliance on paired datasets as well as coarseness in the rendering of facial detail features. especially in non-paired sample scenarios, the difficulty increases remarkably for the synthesis of high-quality sketch facial images. to address these issues, an unpaired facial sketch synthesis method based on a hierarchical contrastive generative adversarial network was proposed. within the network architecture, a global sketch synthesis module was designed to synthesize sketch representations of facial features while maintaining harmonious coordination among different facial regions. a local sketch refinement module was introduced to enhance the portrayal of local details, effectively mitigating the distortion of these intricate features. furthermore, the concept of local refinement loss was introduced to provide local optimization constraints to enhance the realism of the synthesized sketch, particularly in finer details. a series of ablation experiments and comparative studies were conducted on the cufs dataset to evaluate the effectiveness of each component within the framework. the experimental results conclusively demonstrate that the proposed approach yields superior quantitative metrics under non-paired input conditions. the generated sketches exhibit greater realism in intricate details, and the transitions between different facial components appear more natural, resulting in enhanced overall visual appeal.

keywords： face sketch synthesis unpaired learning generative adversarial network hierarchical contrast network