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Multi-person 3D Pose Estimation from Monocular Image Sequences

机译:从单眼图像序列进行多人3D姿势估计

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This article tackles the problem of multi-person 3D human pose estimation based on monocular image sequence in a three-step framework: (1) we detect 2D human skeletons in each frame across the image sequence; (2) we track each person through the image sequence and identify the sequence of 2D skeletons for each person; (3) we reconstruct the 3D human skeleton for each person from the detected 2D human joints, by using prelearned base poses and considering the temporal smoothness. We evaluate our framework on the Hurnan3.6M dataset and the multi-person image sequence captured by ourselves. The quantitative results on the Human3.6M dataset and the qualitative results on our constructed test data demonstrate the effectiveness of our proposed method.
机译:本文在三步框架中解决了基于单眼图像序列的多人3D人体姿势估计问题:(1)我们在整个图像序列的每一帧中检测2D人体骨骼; (2)我们通过图像序列跟踪每个人,并确定每个人的2D骨架序列; (3)通过使用预先学习的基本姿势并考虑时间平滑度,从检测到的2D人体关节为每个人重建3D人体骨骼。我们在Hurnan3.6M数据集和我们自己捕获的多人图像序列上评估我们的框架。在Human3.6M数据集上的定量结果和在我们构建的测试数据上的定性结果证明了我们提出的方法的有效性。

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