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arXiv · 1603.07076

Towards Viewpoint Invariant 3D Human Pose Estimation

Albert Haque·Boya Peng·Zelun Luo·Alexandre Alahi·Serena Yeung·Li Fei-Fei
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Abstract

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.

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Albert Haque, Boya Peng, Zelun Luo, Alexandre Alahi, Serena Yeung, Li Fei-Fei. 2016-03-23. Towards Viewpoint Invariant 3D Human Pose Estimation. https://arxiv.org/abs/1603.07076

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