arXiv 1904.045712019-04-09cs.CV

3DPeople：服装人体几何建模

3DPeople: Modeling the Geometry of Dressed Humans

Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙高等科学研究委员会-加泰罗尼亚理工大学机器人与工业信息研究所）；Harvard University（哈佛大学）

Albert Pumarola, Jordi Sanchez, Gary P. T. Choi, Alberto Sanfeliu, Francesc Moreno-Noguer

摘要：

3D人体形状估计的最新进展建立在参数表示的基础上，该参数表示非常好地模拟了裸体的形状，但不适合表示衣服的几何形状。在本文中，我们提出了一种方法来模拟穿着的人，并预测他们的几何形状从单一的图像。我们在这个问题的三个基本方面做出了贡献，即一个新的数据集，一个新的形状参数化算法和一个用于预测形状的端到端深度生成网络。首先，我们展示了3DPeople，这是一个大规模的合成数据集，包含80个主题的250万张照片般逼真的图像，这些主题正在进行70种活动，并穿着不同的服装。除了为衣服和身体提供纹理化的3D网格外，我们还使用分割掩码、骨架、深度、法线映射和光流对数据集进行注释。所有这些都使3DPeople适合于大量的任务。然后，我们使用2D几何图像表示3D形状。为了建立这些图像，我们提出了一种新的球面面积保持参数化算法的基础上的最佳质量运输方法。我们展示了这种方法来改进现有的球形地图，这些地图倾向于缩小全身模型的细长部分，如手臂和腿，使几何图像不完整。最后，我们设计了一个多分辨率深度生成网络，给定一个穿着衣服的人的输入图像，以端到端的方式预测他/她的几何图像（以及穿着的体型）。我们获得了非常有前途的结果，在联合捕捉身体姿势和服装形状，无论是合成验证和野生图像。

英文摘要：

Recent advances in 3D human shape estimation build upon parametric representations that model very well the shape of the naked body, but are not appropriate to represent the clothing geometry. In this paper, we present an approach to model dressed humans and predict their geometry from single images. We contribute in three fundamental aspects of the problem, namely, a new dataset, a novel shape parameterization algorithm and an end-to-end deep generative network for predicting shape. First, we present 3DPeople, a large-scale synthetic dataset with 2.5 Million photo-realistic images of 80 subjects performing 70 activities and wearing diverse outfits. Besides providing textured 3D meshes for clothes and body, we annotate the dataset with segmentation masks, skeletons, depth, normal maps and optical flow. All this together makes 3DPeople suitable for a plethora of tasks. We then represent the 3D shapes using 2D geometry images. To build these images we propose a novel spherical area-preserving parameterization algorithm based on the optimal mass transportation method. We show this approach to improve existing spherical maps which tend to shrink the elongated parts of the full body models such as the arms and legs, making the geometry images incomplete. Finally, we design a multi-resolution deep generative network that, given an input image of a dressed human, predicts his/her geometry image (and thus the clothed body shape) in an end-to-end manner. We obtain very promising results in jointly capturing body pose and clothing shape, both for synthetic validation and on the wild images.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

摘要：

英文摘要：

相关论文