Viewpoint Invariant 3D Human Pose Estimation with Recurrent Error Feedback
Year: 2016
Type: preprint
Abstract: We propose a viewpoint invariant model for 3D human pose estimation from asingle 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.
Source: arXiv (Cornell University)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2507228759