The application relates to the technical field of paper-
cut design, and discloses a paper-
cut design pattern line reconstruction generation method and
system based on
deep learning, which comprises the following steps:
scribble preprocessing is performed on an original paper-
cut design drawing to draw a
scribble picture, paper-cut patterns, geometric features and texture features are collected to construct a prior
knowledge base; preprocessing features, prior knowledge and latent feature representation are input into a
ControlNet-GAN model; a generator of a GAN network analyzes a conditional
tensor through a
Transformer layer, a Control-UNet layer is integrated into a
convolution module and an attention module of the generator, and a reconstructed paper-cut pattern design drawing is generated; a
discriminator extracts pattern features in the reconstructed paper-cut pattern design drawing, compares the pattern features with the prior
knowledge base, and punishes a generated result that does not conform to paper-cut rules; the application optimizes a
ControlNet algorithm, integrates prior knowledge of paper-cut patterns, makes the generated image conform to paper-cut pattern rules, improves the generation efficiency and quality of a paper-cut design drawing, and meets the digital creation needs of a traditional paper-cut process.