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Type: Conference paper
Title: Adversarial PoseNet: a structure-aware convolutional network for human pose estimation
Author: Chen, Y.
Shen, C.
Wei, X.
Liu, L.
Yang, J.
Citation: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2017), 2017 / vol.2017, pp.1221-1230
Publisher: IEEE
Publisher Place: Piscataway, NJ
Issue Date: 2017
Series/Report no.: IEEE International Conference on Computer Vision
ISBN: 9781538610336
ISSN: 1550-5499
Conference Name: IEEE International Conference on Computer Vision (ICCV 2017) (22 Oct 2017 - 29 Oct 2017 : Venice, ITALY)
Statement of
Yu Chen, Chunhua Shen, Xiu-Shen Wei, Lingqiao Liu, Jian Yang
Abstract: For human pose estimation in monocular images, joint occlusions and overlapping upon human bodies often result in deviated pose predictions. Under these circumstances, biologically implausible pose predictions may be produced. In contrast, human vision is able to predict poses by exploiting geometric constraints of joint inter-connectivity. To address the problem by incorporating priors about the structure of human bodies, we propose a novel structure-aware convolutional network to implicitly take such priors into account during training of the deep network. Explicit learning of such constraints is typically challenging. Instead, we design discriminators to distinguish the real poses from the fake ones (such as biologically implausible ones). If the pose generator (G) generates results that the discriminator fails to distinguish from real ones, the network successfully learns the priors.,,To better capture the structure dependency of human body joints, the generator G is designed in a stacked multi-task manner to predict poses as well as occlusion heatmaps. Then, the pose and occlusion heatmaps are sent to the discriminators to predict the likelihood of the pose being real. Training of the network follows the strategy of conditional Generative Adversarial Networks (GANs). The effectiveness of the proposed network is evaluated on two widely used human pose estimation benchmark datasets. Our approach significantly outperforms the state-of-the-art methods and almost always generates plausible human pose predictions.
Rights: © 2017 IEEE
RMID: 0030083938
DOI: 10.1109/ICCV.2017.137
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Appears in Collections:Computer Science publications

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