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Generative 3D face models are a powerful tool in computer vision. They provide pose and illumination invariance by modeling the space of 3D faces and the imaging process. The power of these models comes at the cost of an expensive and tedious construction process , which has led the community to focus on more easily constructed but less powerful models.(More)
The registration of 3D scans of faces is a key step for many applications, in particular for building 3D Mor-phable Models. Although a number of algorithms are already available for registering data with neutral expression , the registration of scans with arbitrary expressions is typically performed under the assumption of a known, fixed identity. We(More)
We propose a novel model-free pose estimation algorithm to estimate the relative pose of a rigid object. In most pose estimation algorithms, the object of interest covers a large portion of the image. We focus on pose estimation of small objects covering a field of view of less than 5 • by 5 • using stereo vision. With this new algorithm suitable for small(More)
Generative 3D Face Models are expressive models with applications in modelling and editing. They are learned from example faces, and offer a compact representation of the continuous space of faces. While they have proven to be useful as strong priors in face reconstruction they remain to be difficult to use in artistic editing tasks. We describe a way to(More)
The thesis studies the detection of oncoming vehicles in traffic scenes by using depth information. The image sequences in our experiments are captured by a pair of stereo cameras which are mounted in a test vehicle. The main difficulty is to build a system that runs in real time on a standard PC and performs accurate detection of vehicles even under(More)
Reconstructing a person's face from its skeletal remains is a task that has over many decades fascinated artist and scientist alike. In this paper we treat facial reconstruction as a machine learning problem. We use separate statistical shape models to represent the skull and face morphology. We learn the relationship between the parameters of the models by(More)
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