Unpaired sketch face synthesis based on hierarchical contrast generative adversarial network
Current facial sketch synthesis methods suffer from excessive reliance on paired datasets as well as coarseness in the ren-dering 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 con-trast generative adversarial network(HCGAN)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 distor-tion of these intricate features.Furthermore,the concept of local refinement loss was introduced to provide local optimization con-straints to enhance the realism of the synthesized sketch,particularly in finer details.A series of ablation experiments and compara-tive studies were conducted on the CUFS dataset to evaluate the effectiveness of each component within the framework.The experi-mental results conclusively demonstrate that the proposed approach yields superior quantitative metrics under unpaired input condi-tions.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.
face sketch synthesisunpaired learninggenerative adversarial networkhierarchical contrast network
NETL
NSTL
万方数据
