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。

通过相机解耦表示轻量级多视角三维姿态估计

Lightweight Multi-View 3D Pose Estimation through Camera-Disentangled  Representation

3D human articulated pose recovery from monocular image sequences is very
challenging due to the diverse appearances, viewpoints, occlusions, and also
the human 3D pose is inherently ambiguous from the monocular imagery. It is
thus critical to exploit rich spatial and temporal long-range dependencies
among body joints for accurate 3D pose sequence prediction. Existing approaches
usually manually design some elaborate prior terms and human body kinematic
constraints for capturing structures, which are often insufficient to exploit
all intrinsic structures and not scalable for all scenarios. In contrast, this
paper presents a Recurrent 3D Pose Sequence Machine(RPSM) to automatically
learn the image-dependent structural constraint and sequence-dependent temporal
context by using a multi-stage sequential refinement. At each stage, our RPSM
is composed of three modules to predict the 3D pose sequences based on the
previously learned 2D pose representations and 3D poses: (i) a 2D pose module
extracting the image-dependent pose representations, (ii) a 3D pose recurrent
module regressing 3D poses and (iii) a feature adaption module serving as a
bridge between module (i) and (ii) to enable the representation transformation
from 2D to 3D domain. These three modules are then assembled into a sequential
prediction framework to refine the predicted poses with multiple recurrent
stages. Extensive evaluations on the Human3.6M dataset and HumanEva-I dataset
show that our RPSM outperforms all state-of-the-art approaches for 3D pose
estimation.

本文介绍了一种基于递归 3D 姿态序列机器（RPSM）的方法，利用多级顺序细化来自动学习图像相依结构约束和序列相依时间上下文，以提高准确的 3D 姿态序列预测，在人类行为数据集 Human3.6M 和 HumanEva-I 数据集方面的评估显示，我们的方法优于所有最先进的 3D 姿态估计方法。

循环 3D 姿势序列机

Recurrent 3D Pose Sequence Machines

We propose an efficient approach to exploiting motion information from
consecutive frames of a video sequence to recover the 3D pose of people.
Previous approaches typically compute candidate poses in individual frames and
then link them in a post-processing step to resolve ambiguities. By contrast,
we directly regress from a spatio-temporal volume of bounding boxes to a 3D
pose in the central frame.
We further show that, for this approach to achieve its full potential, it is
essential to compensate for the motion in consecutive frames so that the
subject remains centered. This then allows us to effectively overcome
ambiguities and improve upon the state-of-the-art by a large margin on the
Human3.6m, HumanEva, and KTH Multiview Football 3D human pose estimation
benchmarks.

本文提出了一种高效利用视频序列中连续帧运动信息来恢复人的三维姿态的方法，并通过回归边界框的时空体到中心帧的 3D 姿势来改进现有方法。同时，为了最大限度地发挥这种方法的潜力，本文阐明必须补偿连续帧中的运动，以使被测量者保持中心，从而能够有效地消除歧义并在 Human3.6m、HumanEva 和 KTH Multiview Football 3D 人体姿态估计基准测试中取得显著改进。