Automatically determining three-dimensional human pose from monocular RGB
image data is a challenging problem. The two-dimensional nature of the input
results in intrinsic ambiguities which make inferring depth particularly
difficult. Recently, researchers have demonstrated that the flexible
statistical modelling capabilities of deep neural networks are sufficient to
make such inferences with reasonable accuracy. However, many of these models
use coordinate output techniques which are memory-intensive, not
differentiable, and/or do not spatially generalise well. We propose
improvements to 3D coordinate prediction which avoid the aforementioned
undesirable traits by predicting 2D marginal heatmaps under an augmented
soft-argmax scheme. Our resulting model, MargiPose, produces visually coherent
heatmaps whilst maintaining differentiability. We are also able to achieve
state-of-the-art accuracy on publicly available 3D human pose estimation data.

本文介绍了一种改进的三维人体姿势预测模型 MargiPose，它使用深度神经网络和增强软最大值方案，可以在保持可微性的同时，从单眼 RGB 图像中预测二维边缘热图，并在公开的三维人体姿势估计数据上实现了最新的准确性。