From prediction to measurement, an efficient method for digital human model obtainment

Abstract

Digital human has been increasingly used in industry, for example in Metaverse which has been a popular topic in recent years. The existing method of obtaining digital human models are either expensive or lack of accuracy. In this paper, we discuss a novel method to reconstruct a 3D human model from 2D images captured by a monocular camera. The input of our method only requires a set of rotated human body images that can accept slight movement. First, we apply a deep learning method to predict an initial 3D human body model from multi-view human body images. Then the total detailed digital human model will be computed and optimized. The typical method requires the human body and cameras fixed to obtain a visual hull from a significant number of camera images. This could be extremely expensive and inconvenient when such an application is developed for online users. Compared to the structural lighting measurement system, our predict-optimized framework only requires several input images captured by personal equipment to provide enough accuracy and online use resolution results.