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Visual 3D Modeling from Images and Videos - a tech-report describes the theory, practice and tricks on 3D reconstruction from images and videos. Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes with Deep Generative Networks - Generate and reconstruct 3D shapes via modeling multi-view depth maps or silhouettes.
2D to 3D video conversion (also called 2D to stereo 3D conversion and stereo conversion) is the process of transforming 2D ("flat") film to 3D form, which in almost all cases is stereo, so it is the process of creating imagery for each eye from one 2D image.
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]
Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, [128] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts [129] and reweighting training data. [130]
Depth maps can be generated by 3D scanners [4] or reconstructed from multiple images. [5] In Machine vision and computer vision, to allow 3D images to be processed by 2D image tools. Generating and reconstructing 3D shapes from single or multi-view depth maps or silhouettes [3] Making depth image datasets. [6]
2D digital image acquisition is the information source of 3D reconstruction. Commonly used 3D reconstruction is based on two or more images, although it may employ only one image in some cases. There are various types of methods for image acquisition that depends on the occasions and purposes of the specific application.
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