纺织学报 ›› 2025, Vol. 46 ›› Issue (04): 129-137.doi: 10.13475/j.fzxb.20240504501

• 染整工程 • 上一篇    下一篇

基于人工智能生成内容技术的八达晕纹二维图像及三维模型生成方法

茅佳怡1,2,3, 苏淼1,2,3()   

  1. 1.浙江理工大学 纺织科学与工程学院(国际丝绸学院), 浙江 杭州 310018
    2.浙江理工大学 国际丝绸与丝绸之路研究中心, 浙江 杭州 310018
    3.浙江理工大学嵊州创新研究院, 浙江 绍兴 311800
  • 收稿日期:2024-05-20 修回日期:2024-09-14 出版日期:2025-04-15 发布日期:2025-06-11
  • 通讯作者: 苏淼(1980—),女,教授,博士。主要研究方向为丝绸之路与传统丝绸技艺、纺织品设计与数字化。E-mail:sumiao2008@qq.com
  • 作者简介:茅佳怡(2000—),女,硕士生。主要研究方向为丝绸历史及其数字化应用。
  • 基金资助:
    国家重点研发计划项目(2019YFC1521301);浙江文化研究工程课题项目(21WH700992Z)

2-D image and 3-D model generation method of Badayun patterns based on artificial intelligence generated content technology

MAO Jiayi1,2,3, SU Miao1,2,3()   

  1. 1. College of Textile Science and Engineering(International Institute of Silk), Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. International Center for Silk and Silk Road Studies, Hangzhou, Zhejiang 310018, China
    3. Zhejiang Sci-Tech University Shengzhou Innovation Research Institute, Shaoxing, Zhejiang 311800, China
  • Received:2024-05-20 Revised:2024-09-14 Published:2025-04-15 Online:2025-06-11

摘要:

为传承活化八达晕纹的形式美感和文化寓意,突破纹样设计与展示的新维度,提出了一种基于人工智能生成内容(AIGC)技术将纹样从二维图像生成到三维模型转化的生成设计路径。首先剖析八达晕纹的发展历程和艺术风格,重绘其单元纹样构成数据集;接着在训练八达晕纹LoRA模型的基础上,结合ControlNet中的Canny、Depth算法在Stable Diffusion上实现二维八达晕的创新生成,同时采用IP-Adapter模型完成纹样色彩风格迁移;随后依托三维大模型Tripo完成创新纹样的三维模型转换,最后利用增强现实(AR)技术将八达晕纹的文献记载、织物原图等信息同三维模型一起应用于面料上展示。结果表明,该方法实现了八达晕纹的个性化和智能化生成,有助于丝绸文化绵延赓续和技艺传承,同时也对其它传统纹样数字化创新生成具有重要的借鉴意义。

关键词: 八达晕纹, 传承活化, 纹样数字化, 生成设计, 人工智能生成内容, 三维数字技术, 印花图案

Abstract:

Objective Badayun pattern has a long history and contains rich traditional artistic attainments. However, due to the relatively fixed theme and form, its development in the field of modern design is limited. At present, the digital innovation of traditional patterns shows a trend of diversification. This research aims to inherit and activate the formal beauty and cultural meaning of Badayun pattern, and proposes a generation design path based on artificial intelligence-generated content(AIGC) technology to transform the pattern from 2-D image generation to 3-D model transformation, using modern technology to creatively inherit silk culture.

Method Firstly, the development process and artistic style of the Badayun pattern were analyzed, and the data set of its unit pattern was redrawn. Then, on the basis of training the LoRA model, combining with canny edge detection and depth algorithm in controlnet network, the innovation generation of 2-D Badayun pattern was realized on stable diffusion, and the IP-adapter model was used to complete the color style transfer of pattern. The 3-D model conversion of innovative patterns was completed based on 3-D large model Tripo, and the literature record, historical origin and 3-D model of the Badayun pattern to the AR display of fabrics were applied using virtual reality technology.

Results This research facilitated the effect of text to image, image to image, image to 3-D model and 3-D model to AR display, and this method was used to achieve the personalized and intelligent generation of Badayun pattern. In the process of preparing the training set of Badayun pattern, 35 unit structure patterns of 2 400×2 400 pixels were restored in the procreate drawing software, and they were input to the LoRA model training after the normalization of the images. The experimental results showed that in the training process of LoRA model, deepbooru generates labels and processes them manually, which were superior to BLIP labeling processing. In the aspect of traditional theme pattern generation, the XYZ chart finds that the results Epochs=9 training rounds were better than that with lower LOSS version. In the aspect of innovative theme pattern generation, ControlNet assisted the generation of effects to achieve the integration of modern elements. In the aspect of style transfer, when western modern art style brutalism and expressionism painting works were transferred to the Badayun pattern, the generation effect of the IP adapter was better than that of the convolutional neural network VGG-19 network, which offered insights of the elements to be fused, and retained the shape and structure of the Badayun pattern. The selection of random seeds made the generation result diverse. In the aspect of 3-D Badayun pattern generation, three types of 3-D generation models, Tripo, TripoSR, and Meshy, were compared. The results showed that the Tripo model was more suitable for the model generation from a single image to 3-D model from the perspective of texture, geometric structure, and fineness. Finally, the 3-D Badayun pattern was applied to the virtual AR interaction on the fabric based on the Kivicube platform. This intelligent generation path contributes to the continuity of silk culture and the inheritance of silk weaving skills and has important reference significance for the digital innovation generation of other traditional patterns.

Conclusion The pattern generation method based on AIGC technology has achieved the transformation of Badayun patterns from “flat to three-dimensional to scene”. This digital presentation of patterns with the characteristics of the times has promoted the further development of silk pattern design in the field of fashion and technology. With the continuous breakthrough and iteration of artificial intelligence technology, the diversified innovative ways of Badayun patterns have ignited the inheritance fire, making the patterns glow with new vitality. In the future research, multi-modal interaction can be introduced into the application of generating patterns, and sensors can be embedded in the innovative theme Badayun brocade to carry out the design and development of traditional silk pattern theme games with the combination of virtual and real. Connecting history and future through virtual 3-D technology, it expands the application field of traditional patterns, and spreads the beauty of patterns across thousands of years to the world while enriching cultural heritage.

Key words: Badayun pattern, inheritance and activation, pattern digitization, generating design, artificial intelligence-generated content, 3-D digital technology, printed pattern

中图分类号: 

  • TS941.26

图1

经典八达晕纹的纹样单元分析"

图2

不同生成方法的八达晕纹效果"

图3

不同标注方式生成效果对比"

图4

损失率与训练轮次的关系"

图5

原始题材LoRA模型生成效果"

图6

创意题材生成效果"

图7

不同色彩主题生成效果"

图8

基于IP-Adapter的八达晕纹风格迁移架构 注:Z为矩阵向量;T为去噪轮数;E为编码器;D为解码器。"

图9

不同方法风格迁移效果"

图10

不同三维生成模型效果"

图11

三维八达晕纹模型效果"

图12

虚拟现实技术在立体纹样中的应用"

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