While the voxel-based methods have achieved promising results for
multi-person 3D pose estimation from multi-cameras, they suffer from heavy
computation burdens, especially for large scenes. We present Faster VoxelPose
to address the challenge by re-projecting the feature volume to the three
two-dimensional coordinate planes and estimating X, Y, Z coordinates from them
separately. To that end, we first localize each person by a 3D bounding box by
estimating a 2D box and its height based on the volume features projected to
the xy-plane and z-axis, respectively. Then for each person, we estimate
partial joint coordinates from the three coordinate planes separately which are
then fused to obtain the final 3D pose. The method is free from costly 3D-CNNs
and improves the speed of VoxelPose by ten times and meanwhile achieves
competitive accuracy as the state-of-the-art methods, proving its potential in
real-time applications.

本论文提出了 Faster VoxelPose 方法，通过在三个二维坐标平面上重投影特征体积并分别从其中估计 X、Y、Z 坐标，以解决多人三维姿态估计的计算负担过重问题。该方法可以在不需要昂贵的三维卷积神经网络的情况下，使 VoxelPose 的速度提高十倍，同时达到与最先进方法相当的准确性，显示了其在实时应用中的潜力。