Collar Data Synthesis and Its Application for Generating Virtual Collar 3D Novel View from a Single-View

Na Guo, Pei-Wei Tsai, Hongwang Xiao, Chang Wu, Wenjuan Duan

Abstract: In recent years, virtual reality, big data and cloud computing technologies have all made rapid development, and they are also more and more closely combined with the apparel industry. The apparel digital design and virtual fitting techniques are playing increasingly important roles in the field of apparel design, manufacturing and sales. The three-dimensional digital design and virtual fitting make the designers or the users have multi-view and more real impressions and experiences of the apparel. But 3D display is difficult to be popularized in apparel industry because of its high cost. The cost of two-dimensional display is low and easy to popularize. However, 2D system can only provide images of a specific perspective, lacking a multi-perspective experience. If the supplier is required to provide 2D images of several specific perspectives, the personalized needs of users cannot be satisfied. Therefore, how to make 2D system according to an object image and users' needs to provide the object images of other perspectives is an urgent problem to be solved. In this work, we propose a novel view synthesis pipeline based on an enhanced Pix2Pix neural network for a novel view (Pix2Pix-V). It consists of four parts: 1) a new method of generating the labeled virtual collar image data based on its 3D model, and the 2D images can be obtained on any perspective; 2) Pix2Pix-V neural network; 3) a Pix2Pix-V training workflow for predicting the novel view; 4) transfer learning is used in predicting similar views of virtual collars.