Abstract
3D content creation is referred to as one of the most fundamental tasks of computer graphics. And many 3D modeling algorithms from 2D images or curves have been developed over the past several decades. Designers are allowed to align some conceptual images or sketch some suggestive curves, from front, side, and top views, and then use them as references in constructing a 3D model automatically or manually. However, to the best of our knowledge, no studies have investigated on 3D human body reconstruction in a similar manner. In this paper, we propose a deep learning based reconstruction of 3D human body shape from 2D orthographic views. A novel CNN-based regression network, with two branches corresponding to frontal and lateral views respectively, is designed for estimating 3D human body shape from 2D mask images. We train our networks separately to decouple the feature descriptors which encode the body parameters from different views, and fuse them to estimate an accurate human body shape. In addition, to overcome the shortage of training data required for this purpose, we propose some significantly data augmentation schemes for 3D human body shapes, which can be used to promote further research on this topic. Extensive experimen- tal results demonstrate that visually realistic and accurate reconstructions can be achieved effectively using our algorithm. Requiring only binary mask images, our method can help users create their own digital avatars quickly, and also make it easy to create digital human body for 3D game, virtual reality, online fashion shopping.
Abstract (translated)
3D内容创作被称为计算机图形学最基本的任务之一。过去几十年来，许多2D图像或曲线的3D建模算法已经开发出来。设计师可以将一些概念性图像对齐，或从正面，侧面和顶部视图中勾画出一些暗示曲线，然后将它们用作自动或手动构建3D模型的参考。然而，据我们所知，没有研究以类似的方式研究3D人体重建。在本文中，我们提出了一种基于2D正交视图的基于深度学习的3D人体形状重建。设计了一种新颖的基于CNN的回归网络，其中两个分支分别对应于正面和侧面视图，用于从2D掩模图像估计3D人体形状。我们分别训练我们的网络，以解耦来自不同视图的编码身体参数的特征描述符，并将它们融合以估计准确的人体形状。此外，为了克服为此目的所需的训练数据的不足，我们提出了一些针对3D人体形状的显着数据增强方案，其可用于促进对该主题的进一步研究。广泛的实验结果表明，使用我们的算法可以有效地实现逼真和准确的重建。我们的方法只需要二进制蒙版图像，可以帮助用户快速创建自己的数字头像，并且还可以轻松地为3D游戏，虚拟现实，在线时尚购物创建数字人体。
URL
https://arxiv.org/abs/1806.08485
