We propose a key point-based approach, refers to as KPhub-PC, to estimate high-fidelity human body models from low quality point clouds acquired with an affordable 3D scanner and a variation KPhub-I that can achieve the same purpose based on low-resolution single images taken by smartphones. In KPhub-PC, a sparse set of key points is annotated to guide the deformation of a parametric 3D human body model SMPL and then a high-fidelity human body model that can explain the target point cloud is built. Besides building 3D human body models from point clouds, KPhub-I is designed to estimate accurate 3D human body models from single 2D images. The SMPL model is fitted to 2D joints and the boundary of the human body which are detected using CNN based methods automatically. Considering that people are in stable poses at most of the time, a stable pose prior is defined from CMU motion capture dataset for further improving accuracy. Intensive experiments demonstrate that in both types of user-generated data, the proposed approaches can build believable and animatable human body models robustly. Our approach outperforms the state-of-the-arts in the accuracy of both human body shape and pose estimation.

中文翻译：

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从用户生成的数据构建高保真人体模型

我们提出了一种基于关键点的方法，称为 KPhub-PC，从使用负担得起的 3D 扫描仪和可以实现相同目的的变体 KPhub-I 获得的低质量点云中估计高保真人体模型。 - 智能手机拍摄的分辨率单张图像。在 KPhub-PC 中，通过标注一组稀疏的关键点来指导参数化 3D 人体模型 SMPL 的变形，然后构建可以解释目标点云的高保真人体模型。除了从点云构建 3D 人体模型外，KPhub-I 还旨在从单个 2D 图像中估计准确的 3D 人体模型。SMPL 模型适用于使用基于 CNN 的方法自动检测到的 2D 关节和人体边界。考虑到人们大部分时间都处于稳定的姿势，从 CMU 运动捕捉数据集定义了一个稳定的先验姿势，以进一步提高准确性。大量实验表明，在两种类型的用户生成数据中，所提出的方法都可以稳健地构建可信且可动画的人体模型。我们的方法在人体形状和姿势估计的准确性方面都优于最先进的方法。