In this work we address the challenging problem of 3D human pose estimation
from single images. Recent approaches learn deep neural networks to regress 3D
pose directly from images. One major challenge for such methods, however, is
the collection of training data. Specifically, collecting large amounts of
training data containing unconstrained images annotated with accurate 3D poses
is infeasible. We therefore propose to use two independent training sources.
The first source consists of accurate 3D motion capture data, and the second
source consists of unconstrained images with annotated 2D poses. To integrate
both sources, we propose a dual-source approach that combines 2D pose
estimation with efficient 3D pose retrieval. To this end, we first convert the
motion capture data into a normalized 2D pose space, and separately learn a 2D
pose estimation model from the image data. During inference, we estimate the 2D
pose and efficiently retrieve the nearest 3D poses. We then jointly estimate a
mapping from the 3D pose space to the image and reconstruct the 3D pose. We
provide a comprehensive evaluation of the proposed method and experimentally
demonstrate the effectiveness of our approach, even when the skeleton
structures of the two sources differ substantially.

本研究提出一种利用双重数据源，结合深度神经网络和运动捕捉数据，实现从单张图片中估计三维人体姿态的方法，并对该方法进行了全面评估。

单图像三维人体姿态估计的双源方法

A Dual-Source Approach for 3D Human Pose Estimation from a Single Image

One major challenge for 3D pose estimation from a single RGB image is the
acquisition of sufficient training data. In particular, collecting large
amounts of training data that contain unconstrained images and are annotated
with accurate 3D poses is infeasible. We therefore propose to use two
independent training sources. The first source consists of images with
annotated 2D poses and the second source consists of accurate 3D motion capture
data. To integrate both sources, we propose a dual-source approach that
combines 2D pose estimation with efficient and robust 3D pose retrieval. In our
experiments, we show that our approach achieves state-of-the-art results and is
even competitive when the skeleton structure of the two sources differ
substantially.

通过两种独立的训练来源 (2D 姿势标注的图像和准确的 3D 运动捕捉数据)，提出一种双源方法，将 2D 姿态估计与高效稳健的 3D 姿态检索结合，从而解决单个 RGB 图像的 3D 姿态估计中获取足够训练数据的问题，实验表明，该方法达到了最先进的结果，并且即使是两个训练来源中的骨架结构差别显著时也具有竞争性。