论文标题
使用图形卷积网络朝着野外图像的高保真3D面对重建
Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks
论文作者
论文摘要
基于3D形态的模型（3DMM）方法在从单视图像中恢复3D面形形状方面取得了巨大的成功。但是，通过这种方法恢复的面部纹理缺乏输入图像中表现出的忠诚度。最近的工作表明，从高分辨率的紫外线图的大规模数据库中训练的生成网络恢复了高质量的面部纹理，这是很难准备的，并且无法公开可用。在本文中，我们介绍了一种方法，可以重建来自单视图中野外图像的高保真纹理的3D面部形状，而无需捕获大规模的面部纹理数据库。主要思想是完善由基于3DMM的方法生成的初始纹理，其中包含来自输入图像的面部细节。为此，我们建议使用图形卷积网络来重建网格顶点的详细颜色，而不是重建紫外线图。实验表明，我们的方法可以在定性和定量比较中产生高质量的结果，并且胜过最先进的方法。
3D Morphable Model (3DMM) based methods have achieved great success in recovering 3D face shapes from single-view images. However, the facial textures recovered by such methods lack the fidelity as exhibited in the input images. Recent work demonstrates high-quality facial texture recovering with generative networks trained from a large-scale database of high-resolution UV maps of face textures, which is hard to prepare and not publicly available. In this paper, we introduce a method to reconstruct 3D facial shapes with high-fidelity textures from single-view images in-the-wild, without the need to capture a large-scale face texture database. The main idea is to refine the initial texture generated by a 3DMM based method with facial details from the input image. To this end, we propose to use graph convolutional networks to reconstruct the detailed colors for the mesh vertices instead of reconstructing the UV map. Experiments show that our method can generate high-quality results and outperforms state-of-the-art methods in both qualitative and quantitative comparisons.
