Abstract This paper presents a dynamics‐basedEffortsimulator so that a given motion can be modified according to specifiedEffortqualities. The basic idea of our approach is to establish relations betweenEffortfactors such as Space, Weight, Time, and Flow, and their corresponding dynamics parameters such as force, stiffness, inertia, damping, gravity, and etc in the simulator. Then, a motion with specifiedEffortquality can be realized by mapping it to appropriate dynamics parameters of the simulator. The proposed simulator has two merits: it is motion capture‐driven, therefore it is able to synthesize motion details and easily to be incorporated into popular motion capture applications. Besides, it is dynamics based and therefore it provides high‐level control intuition of motion dynamics in specifyingEffortqualities. To demonstrate the effectiveness of the proposed simulator, several experimental examples are presented and their results are discussed. Copyright © 2006 John Wiley & Sons, Ltd.

Abstract Human spontaneous motions such as walking and runing are seldom described in detail. However, some well designed motions such as conditioning and rehabilitation exercises are usually described in detail and edited into exercise manuals for training purpose. This paper presents a novel framework for synthesizing new motions based on given motion manuals and corresponding motion capture examples. First, using text analysis method, a set of basic motion texts is extracted from exercise manuals and converted into text normal form. Then we introduce an annotation method to build the mapping table between basic motion texts and their corresponding motion clips in the given examples. To facilitate the search for proper motion clips, a multi‐dimensional index structure based on posture parameters is proposed. Then a new motion with given textual description can be synthesized by converting the description sentences into a sequence of text normal forms and then concatenating the corresponding motion clips to form the desired motion. Moreover, to realize the smooth concatenation, we show that it can be achieved by finding an appropriate path in the proposed multi‐dimensional index space. Several experimental examples are given to demonstrate the proposed method is effective in synthesizing desired motions according to given descriptions. Copyright © 2004 John Wiley & Sons, Ltd.

Facial model coding is an integral part in MPEG-4 related applications. The generation of the facial model usually requires stereoscopic view of the face in the pre-processing stage. Although facial model can be successfully estimated from two stereo facial images, the occlusion effect and imprecise location of the feature point prohibit obtaining an accurate facial model. In this paper, several facial model estimation (FME) algorithms are proposed in order to find the precise facial model from a stereo or mono image sequence. Since a sequence of images is used to find the facial model, the problem of occlusion effects is less serious. An accurate facial model (within 7.21% error) can still be obtained by our schemes, even without the prior information on the three-dimensional position of the head with respect to the camera and the rotation axis/angle of the head's movement. This is the largest error of all FME algorithms presented in this paper when the subject does not wear eyeglasses. In addition, our schemes do not require precise camera parameters and avoid tedious camera calibration, thereby, simplifying the facial model extraction.

A 3-D facial model can be accurately obtained from: (1) stereo facial images or (2) front- and side-view facial images. The stereo images can be obtained by two different cameras at the same time. On the other hand, capturing front- and side-view facial images requires precise location of the camera. Either may be unavailable in many practical applications. We modify several existing techniques to automatically locate the feature point position from the front-view facial image. Then several schemes (such as minimum mean square error, minimum mean absolute error, and maximum a posteriori) are used to estimate the "depth" information of the 3-D facial model parameters from the single front-view facial image according to the anthropometric and a priori information. With this scheme, a 3-D facial model can be estimated from single front-view facial image. According to the simulation results, the estimated facial model matches the exact 3-D facial model, and the synthesized and original side-view facial images appear similar.

In an object-based analysis-synthesis coder (OBASC) for teleconference, an object can be represented by a model of triangle mesh wireframe. Because the video contents usually are the head-and-shoulders or heads only, the generation of a facial model and its accuracy thus are important issues. A three-dimensional facial model can be generated using a stereo image pair and the knowledge of the disparity. But a facial model reconstructed from a stereo image pair only may be inaccurate because of the occlusion of some features. A new facial model refinement algorithm is proposed in this paper. Here we first generate a rough facial model using the first stereo image pair in a stereo image sequence, and then fine tune this model according to the further information gathered from the following image sequence. Our results converge to the exact facial model and thus provide a better model for the OBASC.

We propose an approach to efficiently compress and transmit multiresolution 3D facial models for multimedia and very low bit rate applications. A personal facial model is obtained by a 3D laser digitizer, and further re-quantized at several resolutions according to different scope of applications, such as animation, video game, and video conference. By deforming 2D templates to match and re-quantize a 3D digitized facial model, we obtain its compressed model. In the present study, we create hierarchical 2D facial wireframe templates at five resolutions. In the deformation procedure, 2D templates are adapted according to facial feature points and the proposed piecewise chainlet affine transformation (PCAT) method. The 3D digitized model after requantization are reduced significantly without perceptual loss. Moreover, the proposed multiresolution facial models possessed a hierarchical data structure and are apt to be progressively transmitted and displayed across the Internet.