Graphics Reference
In-Depth Information
1
3D Face Modeling
Boulbaba Ben Amor,
1
Mohsen Ardabilian,
2
and Liming Chen
2
1
Institut Mines-Telecom/Telecom Lille 1, France
2
Ecole Centrale de Lyon, France
Acquiring, modeling, and synthesizing realistic 3D human faces and their dynamics have
emerged as an active research topic in the border area between the computer vision and
computer graphics fields of research. This has resulted in a plethora of different acquisition
systems and processing pipelines that share many fundamental concepts as well as specific
implementation details. The research community has investigated the possibility of targeting
either end-to-end consumer-level or professional-level applications, such as facial geometry
acquisition for 3D-based biometrics and its dynamics capturing for expression cloning or per-
formance capture and, more recently, for 4D expression analysis and recognition. Despite the
rich literature, reproducing realistic human faces remains a distant goal because the challenges
that face 3D face modeling are still open. These challenges include the motion speed of the
face when conveying expressions, the variabilities in lighting conditions, and pose. In addition,
human beings are very sensitive to facial appearance and quickly sense any anomalies in 3D
geometry or dynamics of faces. The techniques developed in this field attempt to recover facial
3D shapes from camera(s) and reproduce their actions. Consequently, they seek to answer the
following questions:
How can one recover the facial shapes under pose and illumination variations?
How can one synthesize realistic dynamics from the obtained 3D shape sequences?
This chapter provides a brief overview of the most successful existing methods in the
literature by first introducing basics and background material essential to understand them.
To this end, instead of the
classical
passive/active taxonomy of 3D reconstruction techniques,
we propose here to categorize approaches according to whether they are able to acquire faces
in action or they can only capture them in a static state. Thus, this chapter is preliminary to
