Towards Viewpoint Invariant 3D Human Pose Estimation

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 100 K 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.


Editor(s):
Leibe, Bastian
Matas, Jiri
Sebe, Nicu
Welling, Max
Published in:
Computer Vision – ECCV 2016, 9905, 160-177
Year:
2016
Publisher:
Springer
Laboratories:




 Record created 2017-08-21, last modified 2018-01-28

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