标题： SteerPose：基于关节运动的同时外参相机标定与匹配 显示英文标题

标题： SteerPose: Simultaneous Extrinsic Camera Calibration and Matching from Articulation

摘要： 自由移动的人类或动物本身能否作为多摄像机系统的校准目标，同时估计它们在不同视角之间的对应关系？ 我们人类可以通过在心理上旋转观察到的二维姿态，并将其与目标视角中的姿态对齐来解决这个问题。 受这种认知能力的启发，我们提出了 SteerPose，这是一种能够将二维姿态旋转到另一视角的神经网络。 通过集成可微匹配，SteerPose 在单一统一框架内同时执行外参相机标定和对应关系搜索。 我们还引入了一种新颖的几何一致性损失函数，明确确保估计的旋转和对应关系导致有效的平移估计。 在各种野外人类和动物数据集上的实验结果验证了所提出方法的有效性和鲁棒性。 此外，我们展示了我们的方法可以通过利用现成的二维姿态估计器和我们的类别无关模型，在多摄像机设置下重建新型动物的三维姿态。 显示英文摘要

摘要： Can freely moving humans or animals themselves serve as calibration targets for multi-camera systems while simultaneously estimating their correspondences across views? We humans can solve this problem by mentally rotating the observed 2D poses and aligning them with those in the target views. Inspired by this cognitive ability, we propose SteerPose, a neural network that performs this rotation of 2D poses into another view. By integrating differentiable matching, SteerPose simultaneously performs extrinsic camera calibration and correspondence search within a single unified framework. We also introduce a novel geometric consistency loss that explicitly ensures that the estimated rotation and correspondences result in a valid translation estimation. Experimental results on diverse in-the-wild datasets of humans and animals validate the effectiveness and robustness of the proposed method. Furthermore, we demonstrate that our method can reconstruct the 3D poses of novel animals in multi-camera setups by leveraging off-the-shelf 2D pose estimators and our class-agnostic model.