论文标题
具有大规模几何扭曲和内容保存的工业风格转移
Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation
论文作者
论文摘要
我们提出了一种新型的样式转移方法,以快速创建一种新的视觉产品,可为工业设计师的参考提供漂亮的外观。鉴于源产品,目标产品和艺术风格的图像,我们的方法会产生一个神经翘曲场,该神经翘曲场扭曲了源形状,以模仿目标的几何样式和神经纹理转换网络,将艺术风格转移到扭曲的源产品中。我们的模型“工业风格转移”(Inst)由大规模的几何翘曲(LGW)和兴趣一致性纹理转移(ICTT)组成。 LGW的目的是探索源头面罩和目标产品的形状面膜之间的无监督转换,以安装大规模形状翘曲。此外,我们引入了掩盖平滑度正则化项,以防止源产品细节的突然变化。 ICTT引入了一个兴趣正则术语,以通过使用艺术风格的图像对扭曲产品的重要内容进行维护。广泛的实验结果表明,Inst在多个视觉产品设计任务(例如公司的蜗牛徽标和经典瓶子)上实现最新性能(请参见图1)。据我们所知,我们是第一个扩展神经风格转移方法以创建工业产品出现的人。项目页面:\ ulr {https://jcyang98.github.io/inst/home.html}。代码可在:\ url {https://github.com/jcyang98/inst}中获得。
We propose a novel style transfer method to quickly create a new visual product with a nice appearance for industrial designers' reference. Given a source product, a target product, and an art style image, our method produces a neural warping field that warps the source shape to imitate the geometric style of the target and a neural texture transformation network that transfers the artistic style to the warped source product. Our model, Industrial Style Transfer (InST), consists of large-scale geometric warping (LGW) and interest-consistency texture transfer (ICTT). LGW aims to explore an unsupervised transformation between the shape masks of the source and target products for fitting large-scale shape warping. Furthermore, we introduce a mask smoothness regularization term to prevent the abrupt changes of the details of the source product. ICTT introduces an interest regularization term to maintain important contents of the warped product when it is stylized by using the art style image. Extensive experimental results demonstrate that InST achieves state-of-the-art performance on multiple visual product design tasks, e.g., companies' snail logos and classical bottles (please see Fig. 1). To the best of our knowledge, we are the first to extend the neural style transfer method to create industrial product appearances. Project page: \ulr{https://jcyang98.github.io/InST/home.html}. Code available at: \url{https://github.com/jcyang98/InST}.