We propose a viewpoint invariant model for 3D human pose estimation from a
single depth image. To achieve this, our discriminative model embeds local
regions into a learned viewpoint invariant feature space. Formulated as a
multi-task learning problem, our model is able to selectively predict partial
poses in the presence of noise and occlusion. Our approach leverages a
convolutional and recurrent network architecture with a top-down error feedback
mechanism to self-correct previous pose estimates in an end-to-end manner. We
evaluate our model on a previously published depth dataset and a newly
collected human pose dataset containing 100K annotated depth images from
extreme viewpoints. Experiments show that our model achieves competitive
performance on frontal views while achieving state-of-the-art performance on
alternate viewpoints.

提出了一种视角不变的模型，用于从单个深度图像中估计 3D 人体姿态，该模型从一个学习的视角不变特征空间中嵌入局部区域来实现，并采用自下而上的误差反馈机制进行姿态估计，多任务的学习方法可以在噪声和遮挡的情况下选择性地预测局部姿态。通过对一个先前发布的深度数据集和一个包含 10 万个注释深度图像的新收集的人体姿态数据集的评估，实验证明该模型在正面视图上实现了竞争性性能，在替代视角上实现了最先进的性能。