Department of Multimedia Engineering, Dongguk University-Seoul, 30, Pildongro-1-gil, Jung-gu, Seoul 04620, Korea.
Sensors (Basel). 2021 Feb 14;21(4):1350. doi: 10.3390/s21041350.
Applications related to smart cities require virtual cities in the experimental development stage. To build a virtual city that are close to a real city, a large number of various types of human models need to be created. To reduce the cost of acquiring models, this paper proposes a method to reconstruct 3D human meshes from single images captured using a normal camera. It presents a method for reconstructing the complete mesh of the human body from a single RGB image and a generative adversarial network consisting of a newly designed shape-pose-based generator (based on deep convolutional neural networks) and an enhanced multi-source discriminator. Using a machine learning approach, the reliance on multiple sensors is reduced and 3D human meshes can be recovered using a single camera, thereby reducing the cost of building smart cities. The proposed method achieves an accuracy of 92.1% in body shape recovery; it can also process 34 images per second. The method proposed in this paper approach significantly improves the performance compared with previous state-of-the-art approaches. Given a single view image of various humans, our results can be used to generate various 3D human models, which can facilitate 3D human modeling work to simulate virtual cities. Since our method can also restore the poses of the humans in the image, it is possible to create various human poses by given corresponding images with specific human poses.
应用于智慧城市的相关技术在实验开发阶段需要用到虚拟城市。为了构建与现实城市接近的虚拟城市，需要创建大量各种类型的人类模型。为了降低模型获取成本，本文提出了一种从使用普通相机拍摄的单个图像中重建 3D 人体网格的方法。该文提出了一种从单个 RGB 图像和一个新设计的基于形状-姿势的生成对抗网络（基于深度卷积神经网络）和增强型多源鉴别器中重建人体完整网格的方法。通过机器学习方法，减少了对多个传感器的依赖，并且可以使用单个相机恢复 3D 人体网格，从而降低了构建智慧城市的成本。所提出的方法在身体形状恢复方面的准确率达到 92.1%，每秒可以处理 34 张图像。与之前的最先进方法相比，本文提出的方法显著提高了性能。给定各种人的单视图图像，我们的结果可以用于生成各种 3D 人体模型，从而便于进行 3D 人体建模工作以模拟虚拟城市。由于我们的方法还可以恢复图像中人体的姿势，因此可以通过给定具有特定人体姿势的相应图像来创建各种人体姿势。
