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. Code is available at https://jcyang98.github.io/InST/home.html

In this study, we introduce a new factor analysis of Laplacian approach to speaker recognition under the support vector machine (SVM) framework. The Laplacian-projected supervector from our proposed Laplacian approach, which finds an embedding that preserves local information by locality preserving projections (LPP), is believed to contain speaker dependent information. The proposed method was compared with the state-of-the-art total variability approach on 2010 National Institute of Standards and Technology (NIST) Speaker Recognition Evaluation (SRE) corpus. According to the compared results, our proposed method is effective.