Generation of Realistic Virtual Garments on Recovery Human Model

Abstract

Displaying a variety of fabrics on a customized character could help customers choose which fabric is more suitable for themselves and help customers choose clothing. However, it is not an easy task to show realistic garment on customized virtual character. As a result, we propose a stable finite element method (FEM) model which is stable to approximate stretching behaviors. At first, we measure four kinds of cloth materials with measurement techniques to research elastic deformations in real cloth samples. Then, we use the parameter optimization method by fitting the model with measurement data. For promoting the display of realistic fabrics, we recover 3D human in shape and pose from a single image automatically. Human body datasets are constructed at first. Then, CNN-based image retrieval in shape and skeleton-based template matching method in pose are combined for 3D human model recovery. To enrich human body details, we synthesize the human body and 3D face with spatial transformation. We compared our proposed method of recovering 3D human from a single image with the state-of-the-art methods, and the experimental results show that the proposed method allows the recovered virtual human to put on garment with different fabrics and significantly improves the fidelity of virtual garment.