We present a solution to egocentric 3D body pose estimation from monocular
images captured from downward looking fish-eye cameras installed on the rim of
a head mounted VR device. This unusual viewpoint leads to images with unique
visual appearance, with severe self-occlusions and perspective distortions that
result in drastic differences in resolution between lower and upper body. We
propose an encoder-decoder architecture with a novel multi-branch decoder
designed to account for the varying uncertainty in 2D predictions. The
quantitative evaluation, on synthetic and real-world datasets, shows that our
strategy leads to substantial improvements in accuracy over state of the art
egocentric approaches. To tackle the lack of labelled data we also introduced a
large photo-realistic synthetic dataset. xR-EgoPose offers high quality
renderings of people with diverse skintones, body shapes and clothing,
performing a range of actions. Our experiments show that the high variability
in our new synthetic training corpus leads to good generalization to real world
footage and to state of theart results on real world datasets with ground
truth. Moreover, an evaluation on the Human3.6M benchmark shows that the
performance of our method is on par with top performing approaches on the more
classic problem of 3D human pose from a third person viewpoint.

借助底部 VR 设备上安装的鱼眼相机捕捉的单目图像，使用编码器 - 解码器架构和新型多分支解码器，实现了针对自我中心 3D 人体姿势估计的解决方案，并提出了一种大规模的照片逼真的合成数据集 xR-EgoPose。这项工作在合成和实际数据集上与现有算法相比，准确率有大幅提高。