Image-Based 3D Face Modeling System

In Kyu Park,Hui Zhang,Vladimir Vezhnevets

doi:10.1155/asp.2005.2072

Abstract

This paper describes an automatic system for 3D face modeling using frontal and profile images taken by an ordinary digital camera. The system consists of four subsystems including frontal feature detection, profile feature detection, shape deformation, and texture generation modules. The frontal and profile feature detection modules automatically extract the facial parts such as the eye, nose, mouth, and ear. The shape deformation module utilizes the detected features to deform the generic head mesh model such that the deformed model coincides with the detected features. A texture is created by combining the facial textures augmented from the input images and the synthesized texture and mapped onto the deformed generic head model. This paper provides a practical system for 3D face modeling, which is highly automated by aggregating, customizing, and optimizing a bunch of individual computer vision algorithms. The experimental results show a highly automated process of modeling, which is sufficiently robust to various imaging conditions. The whole model creation including all the optional manual corrections takes only 2 3 minutes.

Highlights

The automatic generation of a realistic 3D human head model has been a challenging task in computer vision and computer graphics for many years
We develop a practical automatic system for generating a 3D convincing facial model from frontal and profile images, without imposing strict picture-taking conditions such as illumination, view angle, and camera calibration
This paper describes an image-based 3D face modeling system, which produces a convincing head model

Summary

Introduction

The automatic generation of a realistic 3D human head model has been a challenging task in computer vision and computer graphics for many years. Various applications such as virtual reality, computer games, video conferencing, and 3D animation could benefit from convincing human face models. In order to achieve this, it is necessary to use affordable devices such as a digital camera for data acquisition to maximize the possible level of automation along with the controls over the creation process and to develop robust algorithms that generates plausible results even in the case of imperfect input data. We develop a practical automatic system for generating a 3D convincing facial model from frontal and profile images, without imposing strict picture-taking conditions such as illumination, view angle, and camera calibration.

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Results

Conclusion