With the development of AR/VR technologies, a reliable and straightforward way to digitize a three-dimensional human body is in high demand. Most existing methods use complex equipment and sophisticated algorithms, but this is impractical for everyday users. In this paper, we propose a pipeline that reconstructs a 3D human shape avatar from a single image. Our approach simultaneously reconstructs the three-dimensional human geometry and whole body texture map with only a single RGB image as input. We first segment the human body parts from the image and then obtain an initial body geometry by fitting the segment to a parametric model. Next, we warp the initial geometry to the final shape by utilizing a silhouette-based dense correspondence. Finally, to infer invisible back texture from a frontal image, we propose a network called InferGAN. Based on human semantic information, we also propose a method to handle partial occlusion by reconstructing the occluded body parts separately. Comprehensive experiments demonstrate that our solution is robust and effective on both public and our own datasets. Our human avatars can be easily rigged and animated using MoCap data. We have developed a mobile application that demonstrates this capability for AR applications.

随着AR / VR技术的发展，对三维人体数字化的可靠，直接的方法提出了很高的要求。现有的大多数方法使用复杂的设备和复杂的算法，但这对于日常用户而言是不切实际的。在本文中，我们提出了一种从单个图像重建3D人形化身的管道。我们的方法仅使用单个RGB图像作为输入，即可同时重建三维人体几何图形和全身纹理图。我们首先从图像中分割出人体的各个部分，然后通过将该部分拟合到参数模型中来获得初始的身体几何形状。接下来，我们利用基于轮廓的密集对应关系将初始几何形状变形为最终形状。最后，为了从正面图像推断出不可见的背部纹理，我们提出了一个名为InferGAN的网络。基于人类语义信息，我们还提出了一种通过分别重建被遮挡的身体部位来处理部分遮挡的方法。全面的实验表明，我们的解决方案在公共数据集和我们自己的数据集上都是可靠且有效的。使用MoCap数据可以轻松地对我们的化身进行操纵和制作动画。我们已经开发了一个移动应用程序，该应用程序演示了AR应用程序的这一功能。