A human motion feature based on semi-supervised learning of GMM

Using motion capture to create naturally looking motion sequences for virtual character animation has become a standard procedure in the games and visual effects industry. With the fast growth of motion data, the task of automatically annotating new motions is gaining an importance. In this paper, we present a novel statistic feature to represent each […]

Adaptive multi-view feature selection for human motion retrieval

Human motion retrieval plays an important role in many motion data based applications. In the past, many researchers tended to use a single type of visual feature as data representation. Because different visual feature describes different aspects about motion data, and they have dissimilar discriminative power with respect to one particular class of human motion, […]

Fast individual facial animation framework based on motion capture data

Based upon motion capture, a semi-automatic technique for fast facial animation was implemented. While capturing the facial expressions from a performer, a camera was used to record her/his front face as a texture map. The radial basis function (RBF) technique was utilized to deform a generic facial model and the texture was remapped to generate […]

A semantic feature for human motion retrieval

With the explosive growth of motion capture data, it becomes very imperative in animation production to have an efficient search engine to retrieve motions from large motion repository. However, because of the high dimension of data space and complexity of matching methods, most of the existing approaches cannot return the result in real time. This […]

Professor Jian Jun Zhang