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In this paper, we propose a novel algorithm for reconstructing the 3D shape and texture of human faces from two stereo images, which are captured from calibrated cameras. Our approach works in a sparse to dense manner: we first build a coarse shape estimation based on 3D keypoints, and then use a linear morphable model to efficiently match the detail shape and texture. Compared with the previous works,...
Non-frontal view facial expression recognition is important in many scenarios where the frontal view face images may not be available. However, few work on this issue has been done in the past several years because of its technical challenges and the lack of appropriate databases. Recently, a 3D facial expression database (BU-3DFE database) is collected by Yin et al. [10] and has attracted some researchers...
In this paper, an MPEG4 real-time performance-driven avatar is proposed. The facial motion parameters, namely head rotation angles and translation distances as well as a set of action unit weights, are estimated from live video on a per-frame basis using a two-step robust facial motion tracking algorithm. The estimated parameters are then converted into MPEG4 facial animation parameter (FAP) values...
3D models of large-scale scenes available on the Internet today are largely manually created. Thus it takes a long time to create them for cities and update them as those cities that are already modeled continue to change. Multiple parallel-perspective mosaics can be generated from video automatically and more efficiently and can be used to reconstruct 3D scenes faster. A lot of video currently exists...
In this paper, the problem of person-independent facial expression recognition from 3D facial shapes is investigated. We propose a novel automatic feature selection method based on maximizing the average relative entropy of marginalized class-conditional feature distributions and apply it to a complete pool of candidate features composed of normalized Euclidean distances between 83 facial feature...
This paper presents a framework to analyze a large amount of video data and extract high-level structural information - planar structures and motion information - in typical urban scenes, which may be used in video coding or object recognition. The method consists of two phases. In the first phase, multiple parallel-perspective (pushbroom) mosaics are generated from the video data. In the second phase,...
In this paper, we propose a complete pipeline of efficient and low-cost techniques to construct a realistic 3D text-driven emotive audio-visual avatar from a single 2D frontal-view face image of any person on the fly. This real-time conversion is achieved through three steps. First, a personalized 3D face model is built based on the 2D face image using a fully automatic 3D face shape and texture reconstruction...
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