Abstract
Consumer depth cameras bring about cheap and fast acquisition of 3D models. However, the precision and resolution of these consumer depth cameras cannot satisfy the requirements of some 3D face applications. In this paper, we present a super-resolution method for reconstructing a high resolution 3D face model from a low resolution 3D face model acquired from a consumer depth camera. We used a group of radial curves to represent a 3D face. For a given low resolution 3D face model, we first extracted radial curves on it, and then estimated their corresponding high resolution ones by radial curve matching, for which Dynamic Time Warping (DTW) was used. Finally, a reference high resolution 3D face model was deformed to generate a high resolution face model by using the radial curves as the constraining feature. We evaluated our method both qualitatively and quantitatively, and the experimental results validated our method.
Highlights
In recent years, 3D face modeling has received extensive attention due to its widespread applications in face recognition, animation and 3D video games
Depth information can be transformed into 3D information; i.e., corresponding 3D models of objects can be constructed from depth images
We propose a 3D face model super-resolution method based on radial curves
Summary
3D face modeling has received extensive attention due to its widespread applications in face recognition, animation and 3D video games. The usual way to obtain 3D face models is 3D scanning by some high resolution 3D scanners, such as Artec Eva and Minolta Vivid. These professional 3D scanners are expensive and have a high computational cost. For this reason, some consumer depth cameras, such as Microsoft Kinect and Intel RealSense, have drawn wide attention because of their low cost and easy integration. The precision and resolution of these consumer depth cameras cannot satisfy the requirements of some 3D face applications. How to acquire high precision and high resolution face models fast and cheaply still is a challenging task. Improving the precision and resolution of 3D face models acquired by consumer cameras, i.e., 3D face model super-resolution, is a valuable study
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