VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment

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Abstract

We present an approach to estimate 3D poses of multiple people from multiple camera views. In contrast to the previous efforts which require to establish cross-view correspondence based on noisy and incomplete 2D pose estimations, we present an end-to-end solution which directly operates in the 3D space, therefore avoids making incorrect decisions in the 2D space. To achieve this goal, the features in all camera views are warped and aggregated in a common $3$D space, and fed into \emph{Cuboid Proposal Network} (CPN) to coarsely localize all people. Then we propose \emph{Pose Regression Network} (PRN) to estimate a detailed 3D pose for each proposal. The approach is robust to occlusion which occurs frequently in practice. Without bells and whistles, it outperforms the state-of-the-arts on the public datasets.

Publication
In ECCV 2020 (Oral)
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