多视图匹配（MVM）：使用冻结的人视频促进多人3D姿势估计学习
Multi-View Matching (MVM): Facilitating Multi-Person 3D Pose Estimation Learning with Action-Frozen People Video
摘要：
为了解决从单幅图像中估计多人三维姿态的挑战性问题，本文提出了一种多视点匹配（MVM）方法。MVM方法从大规模视频数据集（称为Mannequin数据集）生成可靠的3D人体姿势，该数据集包含模仿人体模型的动作冻结人。利用MVM自动生成的3D监督标记的大量野外视频数据，我们能够训练一个神经网络，该网络将单个图像作为多人3D姿态估计的输入。MVM的核心技术在于对具有强几何约束的静态场景的多个视图中获得的2D姿态进行有效的对齐。我们的目标是最大限度地提高在多帧中估计的2D姿态的相互一致性，同时考虑几何约束和外观相似性。为了证明MVM方法提供的3D监督的有效性，我们在3DPW和MSCOCO数据集上进行了实验，并表明我们提出的解决方案提供了最先进的性能。
英文摘要：
To tackle the challeging problem of multi-person 3D pose estimation from a single image, we propose a multi-view matching (MVM) method in this work. The MVM method generates reliable 3D human poses from a large-scale video dataset, called the Mannequin dataset, that contains action-frozen people immitating mannequins. With a large amount of in-the-wild video data labeled by 3D supervisions automatically generated by MVM, we are able to train a neural network that takes a single image as the input for multi-person 3D pose estimation. The core technology of MVM lies in effective alignment of 2D poses obtained from multiple views of a static scene that has a strong geometric constraint. Our objective is to maximize mutual consistency of 2D poses estimated in multiple frames, where geometric constraints as well as appearance similarities are taken into account simultaneously. To demonstrate the effectiveness of 3D supervisions provided by the MVM method, we conduct experiments on the 3DPW and the MSCOCO datasets and show that our proposed solution offers the state-of-the-art performance.
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。
