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The 3D Morphable Model (3DMM) is a general framework that has been applied to various objects other than faces, e.g., the whole human body, [3] [4] specific body parts, [5] [6] and animals. [ 7 ] 3DMMs were first developed to solve vision tasks by representing objects in terms of the prior knowledge that can be gathered from that object class.
3D model of a human face. Three-dimensional face recognition (3D face recognition) is a modality of facial recognition methods in which the three-dimensional geometry of the human face is used. It has been shown that 3D face recognition methods can achieve significantly higher accuracy than their 2D counterparts, rivaling fingerprint recognition.
In 1971 Henri Gouraud made the first CG geometry capture and representation of a human face. Modeling was his wife Sylvie Gouraud. The 3D model was a simple wire-frame model and he applied the Gouraud shader he is most known for to produce the first known representation of human-likeness on computer. [3] [4]
The tool is specifically designed for the modeling of virtual 3D human models, with a simple and complete pose system that includes the simulation of muscular movement. The interface is easy to use, with fast and intuitive access to the numerous parameters required in modeling the human form.
FaceGen 3.3 allows the user to randomize, tween, normalize and exaggerate faces, and also includes algorithms for adjusting apparent age, ethnicity and gender. It also allows limited parametric control of facial expressions, and includes a set of phoneme expressions for the animation of characters with "speaking" roles.
The additional constraints of the face also allow more opportunities for using models and rules. Facial expression capture is similar to facial motion capture. It is a process of using visual or mechanical means to manipulate computer generated characters with input from human faces , or to recognize emotions from a user.
A direct predecessor of the StyleGAN series is the Progressive GAN, published in 2017. [9]In December 2018, Nvidia researchers distributed a preprint with accompanying software introducing StyleGAN, a GAN for producing an unlimited number of (often convincing) portraits of fake human faces.
To recognise faces, gallery images – those seen by the system – are saved as collections of weights describing the contribution each eigenface has to that image. When a new face is presented to the system for classification, its own weights are found by projecting the image onto the collection of eigenfaces.