HCGAN: hierarchical contrast generative adversarial network for unpaired sketch face synthesis.

ABSTRACT: Transforming optical facial images into sketches while preserving realism and facial features poses a significant challenge. The current methods that rely on paired training data are costly and resource-intensive. Furthermore, they often fail to capture the intricate features of faces, resulting in substandard sketch generation. To address these challenges, we propose the novel hierarchical contrast generative adversarial network (HCGAN). Firstly, HCGAN consists of a global sketch synthesis module that generates sketches with well-defined global features and a local sketch refinement module that enhances the ability to extract features in critical areas. Secondly, we introduce local refinement loss based on the local sketch refinement module, refining sketches at a granular level. Finally,

SUBMITTER: Du K 

PROVIDER: S-EPMC11322990 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature