1.1 Face modeling using 3D range scanner
Recently, laser-based 3D range scanners have been commercially available.
Examples include Cyberware TM [Cyberware, 2003] scanner, Eyetronics TM
scanner [Eyetronics, 2003], and etc. Cyberware TM scanner shines a safe,
low-intensity laser on a human face to create a lighted profile. A video sensor
captures this profile from two viewpoints. The laser beam rotates around the
face 360 degrees in less than 30 seconds so that the 3D shape of the face can
captured by combining the profiles from every angle. Simultaneously, a sec-
ond video sensor in the scanner acquires color information. Eyetronics TM
scanner shines a laser grid onto the human facial surface. Based on the de-
formation of the grid, the geometry of the surface is computed. Comparing
these two systems, Eyetronics TM is a “one shot” system which can output 3D
face geometry based on the data of a single shot. In contrast, Cyberware TM
scanner need to collect multiple profiles in a full circle which takes more time.
In post-processing stage, however, Eyetronics TM needs more manual adjust-
ment to deal with noisy data. As for the captured texture of the 3D model,
Eyetronics TM has higher resolution since it uses high resolution digital cam-
era, while texture in Cyberware TM has lower resolution because it is derived
from low resolution video sensor. In summary, these two ranger scanners have
different features and can be used to capture 3D face data in different scenarios.
Based on the 3D measurement using these ranger scanners, many approaches
have been proposed to generate 3D face models ready for animation. Ostermann
et al. [Ostermann et al., 1998] developed a system to fit a 3D model using
Cyberware TM scan data. Then the model is used for MPEG-4 face animation.
Lee et al. [Lee et al., 1993, Lee et al., 1995] developed techniques to clean up
and register data generated from Cyberware TM laser scanners. The obtained
model is then animated by using a physically based approach. Marschner et
al. [Marschner et al., 2000] achieved the model fitting using a method built upon
fitting subdivision surfaces.
1.2 Face modeling using 2D images
A number of researchers have proposed to create face models from 2D im-
ages. Some approaches use two orthogonal views so that the 3D information
of facial surface points can be measured [Akimoto et al., 1993, Dariush et al.,
1998, H.S.Ip and Yin, 1996]. They require two cameras which must be carefully
set up so that their directions are orthogonal. Zheng [Zheng, 1994] developed
a system to construct geometrical object models from image contours. The
system requires a turn-table setup. Pighin et al. [Pighin et al., 1998] developed
a system to allow a user to manually specify correspondences across multiple
images, and use computer vision techniques to compute 3D reconstructions of
specified feature points. A 3D mesh model is then fitted to the reconstructed 3D