编织Pix2Pix:一个增强的Pix2Pix网络用于织织布料纹理生成
Xin Ru1,2,3, Yingjie Huang1, Laihu Peng1
1College of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
Knit-Pix2Pix从单元网格图中生成现实的织物纹理,克服了传统方法的扭曲. 这个框架增强了虚拟试用和数字织设计,提高了视觉准确性.
科学领域:
- 计算机视觉 计算机视觉
- 数字织品 数字织品
- 材料科学 是一种材料科学.
背景情况:
- 对于织物编织织物的传统纹理映射,由于在拉伸过程中忽视了线变化,因此与视觉扭曲作斗争.
- 现有的方法依赖于几何变换,无法捕捉复杂的循环形态变化在实时应用程序,如虚拟试用.
研究的目的:
- 推出Knit-Pix2Pix,这是一个创新的框架,可以直接从针织单元网格图中生成现实的织物纹理.
- 通过考虑多尺度特征和变形意识来解决传统纹理映射的局限性.
主要方法:
- 开发了Knit-Pix2Pix,这是一个集成的架构,具有多级特征提取,网格引导的注意力机制和多级歧视器.
- 使用编织单元网格图表示循环变形状态用于纹理生成.
- 创建了一个数据集,包含2000多个织物拉伸图像和网状图对,通过弹质量织物模拟进行验证.
主要成果:
- 与传统方法相比,Knit-Pix2Pix显著提高了纹理现实性.
- 量化指标显示,SSIM增加了21.8%,PSNR增加了20.9%,LPIPS下降了24.3%.
- 该框架有效地捕捉了织物织物的多尺度特征和变形意识要求.
结论:
- Knit-Pix2Pix提供了一种实用且有效的解决方案,用于生成高保真织布质感.
- 拟议的方法提高了先进的数字织设计和虚拟试用应用所需的视觉准确性.
- 这种方法代表了对变形表面实时织物纹理合成的重大进步.
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