论文标题
无监督的3D键点发现具有多视图几何形状
Unsupervised 3D Keypoint Discovery with Multi-View Geometry
论文作者
论文摘要
分析和训练3D身体姿势模型在很大程度上取决于通常通过精心策划的标记和捕获系统的艰苦手动注释或通过基于标记的关节定位来获得的关节标签的可用性。但是，这种注释并不总是可用的，尤其是对于从事异常活动的人们而言。在本文中，我们提出了一种算法，该算法学会从多视图图像上发现人体上的3D关键点，而无需任何监督或标签多视图几何形状提供的限制。为了确保发现的3D关键点有意义，它们会重新投影到每种视图中，以估计模型本身最初估算的无监督的掩蔽。与其他最先进的无监督方法360万和MPI-INF-INF-3DHP基准数据集相比，我们的方法发现了更容易解释和准确的3D关键。
Analyzing and training 3D body posture models depend heavily on the availability of joint labels that are commonly acquired through laborious manual annotation of body joints or via marker-based joint localization using carefully curated markers and capturing systems. However, such annotations are not always available, especially for people performing unusual activities. In this paper, we propose an algorithm that learns to discover 3D keypoints on human bodies from multiple-view images without any supervision or labels other than the constraints multiple-view geometry provides. To ensure that the discovered 3D keypoints are meaningful, they are re-projected to each view to estimate the person's mask that the model itself has initially estimated without supervision. Our approach discovers more interpretable and accurate 3D keypoints compared to other state-of-the-art unsupervised approaches on Human3.6M and MPI-INF-3DHP benchmark datasets.
