论文标题
一种新颖的关节点和基于轮廓的方法，用于估计3D人姿势和形状
A novel joint points and silhouette-based method to estimate 3D human pose and shape
论文作者
论文摘要
本文提出了一种基于参数模型的关节点和剪影，提出了一种用于3D人姿势和形状估计的新方法。首先，参数模型拟合到通过基于深度学习的人姿势估计估计的关节点。然后，我们在2D和3D空间上提取姿势拟合的参数模型和轮廓之间的对应关系。建立并最小化基于对应关系的新型能量函数将参数模型拟合到轮廓。我们的方法使用足够的形状信息，因为轮廓的能量功能是由2D和3D空间构建的。这也意味着我们的方法只需要稀疏视图中的图像，这些图像平衡所使用的数据和所需的先前信息。合成数据和实际数据的结果证明了我们方法在人体的姿势和形状估计上的竞争性能。
This paper presents a novel method for 3D human pose and shape estimation from images with sparse views, using joint points and silhouettes, based on a parametric model. Firstly, the parametric model is fitted to the joint points estimated by deep learning-based human pose estimation. Then, we extract the correspondence between the parametric model of pose fitting and silhouettes on 2D and 3D space. A novel energy function based on the correspondence is built and minimized to fit parametric model to the silhouettes. Our approach uses sufficient shape information because the energy function of silhouettes is built from both 2D and 3D space. This also means that our method only needs images from sparse views, which balances data used and the required prior information. Results on synthetic data and real data demonstrate the competitive performance of our approach on pose and shape estimation of the human body.
