3D Face Morphable Model
In computer vision and computer graphics, the 3D Morphable Model (3DMM) is a generative technique that uses methods of statistical shape analysis to model 3D objects. The model follows an analysis-by-synthesis approach over a dataset of 3D example shapes of a single class of objects (e.g., face, hand). The main prerequisite is that all the 3D shapes are in a dense point-to-point correspondence, namely each point has the same semantical meaning over all the shapes. In this way, we can extract meaningful statistics from the dataset and use it to represent new plausible shapes of the object's class. Given a 2D image, we can represent its 3D shape via a fitting process or generate novel shapes by directly sampling from the statistical shape distribution of that class. The question that initiated the research on 3DMMs was to understand how a visual system could handle the vast variety of images produced by a single class of objects and how these can be represented. The primary assumption ...
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Computer Vision
Computer vision tasks include methods for image sensor, acquiring, Image processing, processing, Image analysis, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the form of decisions. "Understanding" in this context signifies the transformation of visual images (the input to the retina) into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. The scientific discipline of computer vision is concerned with the theory behind artificial systems that extract information from images. Image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanning, 3D scanner, 3D point clouds ...
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