基于AIGC语义驱动的针织负面特征摘除方法OA

Artificial intelligence generated content(AIGC)transforms the productivity of fashion design.This technology evolves from a simple auxiliary tool into a core driver for creative generation and industrial workflow innovation.By enabling rapid style iteration and high-fidelity 3D rendering,platforms like Alibaba's FashionAI and Style3D exemplify how AIGC accelerates the transition from abstract concepts to tangible fashion items. However,general AIGC models encounter technical bottlenecks in the apparel field.They lose information when handling fine-grained features and consequently fail to preserve critical details like texture orientation and fabric structures.Furthermore,a gap exists in semantic consistency:these models often struggle to map professional design terms to visual elements,resulting in outputs that lack technical feasibility and manufacturing logic.This problem is especially significant in knitwear design.Since knitwear depends on complex yarn interlacing,its features—such as texture,elasticity,and stitch density—cause semantic entanglement in the latent space.Thus,the generative results often fail to satisfy the dual requirements of visual realism and structural rationality. Built on the stable diffusion model,the research optimizes a semantic decoupling strategy for knitwear via textual inversion training.By reverse-extracting and refining key textual embeddings,the method isolates negative features—such as structural irregularities and texture artifacts—to achieve a seamless fusion of professional textile knowledge with AIGC capabilities. The research implements a progressive experimental system with three complementary stages.Experiment 1 evaluates the ability of general models to understand professional knitting terms,and identifies the limitations of current semantic mapping in vertical domains.Experiment 2 provides a horizontal comparison of mainstream control methods,measures the performance of different techniques in suppressing negative knitwear features,and establishes the position of TI technology compared to other methods.Experiment 3 verifies the performance of the proposed TI negative embedding strategy in end-to-end tasks.All experiments adopt a consistent environment and evaluation framework to ensure a coherent demonstration chain. The dataset uses real images from DeepFashion2(2019)as a benchmark to compare generated images before and after optimization,with a focus on differences in physical features and aesthetic expression.A fuzzy function then maps algorithmic data,human scores,and process parameters into a unified quality evaluation space.Specifically,to verify the effectiveness of the embedding vectors,the study measures the distance between the latent space representation of the generated image and key conceptual anchors—anchors that represent coarse knit features—thereby quantifying the effect of the semantic decoupling mechanism in the latent space. The research proposes a domain-specific semantic decoupling method that uses textual inversion to control knitwear semantics in the textual embedding space,thereby addressing the semantic entanglement between yarn structure and garment shape.The study defines specific negative semantic tasks for the knitwear field.The results show that the model separates undesirable features from prompts,achieving the directional enhancement of key semantic elements while eliminating negative interference during the generation process. Future research will focus on two directions:extending feature decoupling to complex materials(e.g.,leather,composite fabrics)to enrich AI's design vocabulary,and transcending 2D limitations to develop a collaborative negative semantic framework for textiles in 3D environments,aiming to enable a full-link 3D workflow from conceptual design to physical sample production.