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Linear object classes and image synthesis from a single example image

机译:线性对象类和来自单个示例图像的图像合成

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摘要

The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and object recognition. While the traditional approach relies on the use of 3D models, simpler techniques are applicable under restricted conditions. The approach exploits image transformations that are specific to the relevant object class, and learnable from example views of other "prototypical" objects of the same class. In this paper, we introduce such a technique by extending the notion of linear class proposed by the authors (1992). For linear object classes, it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively "rotate" high-resolution face images from a single 2D view.
机译:从单个真实图像生成3D对象的新视图的需求出现在几个领域,包括图形和对象识别。虽然传统方法依赖于3D模型的使用,但在受限条件下可以应用更简单的技术。该方法利用了特定于相关对象类的图像转换,并且可以从相同类的其他“原型”对象的示例视图中学习。在本文中,我们通过扩展作者(1992年)提出的线性类的概念来介绍这种技术。对于线性对象类,它表明可以从2D原型视图的基础集中精确地学习线性变换。我们在人造物体上演示了该方法,然后显示了该技术可以从单个2D视图有效“旋转”高分辨率面部图像的初步证据。

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