Viewpoint Invariant 3D Human Pose Estimation with Recurrent Error Feedback
We propose a viewpoint invariant model for 3D human pose estimation from a single depth image. To achieve viewpoint invariance, our deep 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 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.
ArXiv