vrn：基于直接体积回归的单幅图像大姿态三维人脸重建

3D面部重建是一个很是困难的基本计算机视觉问题。目前的系统一般假设多个面部图像（有时来自同一主题）做为输入的可用性，而且必须解决许多方法学挑战，例如在大的面部姿式，表情和不均匀照明之间创建密集的对应。通常来讲，这些方法须要复杂和低效的管道来建模和拟合。在这项工做中，咱们提出经过在由2D图像和3D面部模型或扫描组成的适当数据集上训练卷积神经网络（CNN）来解决许多这些限制。咱们的CNN只使用一个2D面部图像，不须要精确的对准，也不会造成图像之间的密集对应，适用于任意面部姿式和表情，并可用于重建整个3D面部几何（包括不可见部分（在训练期间）和拟合（测试期间）3D变形模型。咱们经过一个简单的CNN架构来实现这一点，该架构对单个2D图像的3D面部几何体的体积表示进行直接回归。咱们还展现了如何将面部地标定位的相关任务归入拟议的框架，并有助于提升重建质量，特别是对于大姿式和面部表情的状况。

3D face reconstruction is a fundamental Computer Vision problem of extraordinary difficulty. Current systems often assume the availability of multiple facial images (sometimes from the same subject) as input, and must address a number of methodological challenges such as establishing dense correspondences across large facial poses, expressions, and non-uniform illumination. In general these methods require complex and inefficient pipelines for model building and fitting. In this work, we propose to address many of these limitations by training a Convolutional Neural Network (CNN) on an appropriate dataset consisting of 2D images and 3D facial models or scans. Our CNN works with just a single 2D facial image, does not require accurate alignment nor establishes dense correspondence between images, works for arbitrary facial poses and expressions, and can be used to reconstruct the whole 3D facial geometry (including the non-visible parts of the face) bypassing the construction (during training) and fitting (during testing) of a 3D Morphable Model. We achieve this via a simple CNN architecture that performs direct regression of a volumetric representation of the 3D facial geometry from a single 2D image. We also demonstrate how the related task of facial landmark localization can be incorporated into the proposed framework and help improve reconstruction quality, especially for the cases of large poses and facial expressions.

项目地址：https://github.com/AaronJackson/vrn

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