Construction of Knowledge Graph of 3D Clothing Design Resources Based on Multimodal Clustering Network

Abstract

The construction of 3D design model is a hotspot of applied research in the fields of clothing functional design system teaching and display. The simple 3D clothing visualization postprocessing lacks interactive functions, which is a hot issue that needs to be solved urgently at present. Based on analyzing the existing clothing modeling technology, template technology, and fusion technology, and based on the multimodal clustering network theory, this paper proposes a 3D clothing design resource knowledge graph modeling method with multiple fusion of features and templates. The position of each joint point is converted into the coordinate system centered on the torso point in advance and normalized to avoid the problem that the relative position of the camera and the collector cannot be determined, and the shape of different collectors is different. The paper provides a multimodal clustering network intelligence method, illustrates the interoperability of users switching between different design networks in the seamless connection movement, and combines the hybrid intelligence algorithm with the fuzzy logic interpretation algorithm to solve the problems in the field of 3D clothing design service quality. During the simulation process, the research scheme builds a logical multimodal clustering network framework, which integrates compatibility access and global access partition fusion of style templates to achieve information extraction of clothing parts. The experimental results show that the realistic 3D clothing modeling can be achieved by layering the 3D clothing map, contour features, clothing size features, and color texture features with the modeling template. The developed ActiveX control is mounted on MSN, and the system is compatible. The performance and integration rate reached 77.1% and 89.7%, respectively, which effectively strengthened the practical role of the 3D clothing design system.

MultiCogniGraph: A multimodal data fusion and graph convolutional network‐based multi‐hop reasoning method for large equipment fault diagnosis

“…For instance, kgbench 12 offered multimodal datasets for evaluating RDF‐encoded knowledge graphs, yet practical verification in real‐world scenarios remains scarce. Zheng and Hong devised a method for 3D clothing design resource modeling with multimodal attributes and templates integration, 13 but were constrained by the simplicity of their knowledge graph. Similarly, Liao's evaluation of an English online homework system using a multimodal knowledge graph was limited to text and speech data 5 …”