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The analysis-by-synthesis approach enabled the mapping of the 3D and 2D domains and a new representation of 3D shape and appearance. Their work is the first to introduce a statistical model for faces that enabled 3D reconstruction from 2D images and a parametric face space for controlled manipulation. [2]
The 3D reconstruction of objects is a generally scientific problem and core technology of a wide variety of fields, such as Computer Aided Geometric Design , computer graphics, computer animation, computer vision, medical imaging, computational science, virtual reality, digital media, etc. [3] For instance, the lesion information of the ...
A neural radiance field (NeRF) is a method based on deep learning for reconstructing a three-dimensional representation of a scene from two-dimensional images. The NeRF model enables downstream applications of novel view synthesis, scene geometry reconstruction, and obtaining the reflectance properties of the scene.
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.
A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
3D Face image database. 34 action units and 6 expressions labeled; 24 facial landmarks labeled. 4652 Images, text Face recognition, classification 2008 [105] [106] A Savran et al. UOY 3D-Face neutral face, 5 expressions: anger, happiness, sadness, eyes closed, eyebrows raised. labeling. 5250 Images, text Face recognition, classification 2004 ...
Liu's intrinsic image decomposition research addressed the problem of 3D reconstruction from 2D images, aiming to estimate high-fidelity 3D surface information of objects or scenes. He proposed an approach based on intrinsic image decomposition, breaking down an image into four components: camera projection matrix, shape parameters, albedo ...
By training a CNN on a dataset of images with labeled facial landmarks, the algorithm can learn to detect these landmarks in new images with high accuracy even when they appear in different lighting conditions, at different angles, or in partially occluded views.