We present a lightweight solution to recover 3D pose from multi-view images
captured with spatially calibrated cameras. Building upon recent advances in
interpretable representation learning, we exploit 3D geometry to fuse input
images into a unified latent representation of pose, which is disentangled from
camera view-points. This allows us to reason effectively about 3D pose across
different views without using compute-intensive volumetric grids. Our
architecture then conditions the learned representation on camera projection
operators to produce accurate per-view 2d detections, that can be simply lifted
to 3D via a differentiable Direct Linear Transform (DLT) layer. In order to do
it efficiently, we propose a novel implementation of DLT that is orders of
magnitude faster on GPU architectures than standard SVD-based triangulation
methods. We evaluate our approach on two large-scale human pose datasets (H36M
and Total Capture): our method outperforms or performs comparably to the
state-of-the-art volumetric methods, while, unlike them, yielding real-time
performance.

本文提出了一种基于多视图图像的轻量级解决方案，通过利用新近的可解释表示学习方法，将 3D 几何结构融合到输入图像中，利用摄像机投影运算符对学习表示进行条件化，以产生准确的视角 2D 检测，通过可微的直接线性变换层 (SVD) 将其简单地提升到 3D，并提出一种新方法实现直接线性变换层 (SVD) 以有效地实现三维姿态恢复，同时保证实时性能。