Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition
We propose Human Pose Models that represent RGB and depth images of human poses independent of clothing textures, backgrounds, lighting conditions, body shapes and camera viewpoints. Learning such universal models requires training images where all factors are varied for every human pose. Capturing...
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| Published in: | International journal of computer vision Vol. 127; no. 10; pp. 1545 - 1564 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Springer US
01.10.2019
Springer Springer Nature B.V |
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| ISSN: | 0920-5691, 1573-1405 |
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| Abstract | We propose Human Pose Models that represent RGB and depth images of human poses independent of clothing textures, backgrounds, lighting conditions, body shapes and camera viewpoints. Learning such universal models requires training images where all factors are varied for every human pose. Capturing such data is prohibitively expensive. Therefore, we develop a framework for synthesizing the training data. First, we learn representative human poses from a large corpus of real motion captured human skeleton data. Next, we fit synthetic 3D humans with different body shapes to each pose and render each from 180 camera viewpoints while randomly varying the clothing textures, background and lighting. Generative Adversarial Networks are employed to minimize the gap between synthetic and real image distributions. CNN models are then learned that transfer human poses to a shared high-level invariant space. The learned CNN models are then used as invariant feature extractors from real RGB and depth frames of human action videos and the temporal variations are modelled by Fourier Temporal Pyramid. Finally, linear SVM is used for classification. Experiments on three benchmark human action datasets show that our algorithm outperforms existing methods by significant margins for RGB only and RGB-D action recognition. |
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| AbstractList | We propose Human Pose Models that represent RGB and depth images of human poses independent of clothing textures, backgrounds, lighting conditions, body shapes and camera viewpoints. Learning such universal models requires training images where all factors are varied for every human pose. Capturing such data is prohibitively expensive. Therefore, we develop a framework for synthesizing the training data. First, we learn representative human poses from a large corpus of real motion captured human skeleton data. Next, we fit synthetic 3D humans with different body shapes to each pose and render each from 180 camera viewpoints while randomly varying the clothing textures, background and lighting. Generative Adversarial Networks are employed to minimize the gap between synthetic and real image distributions. CNN models are then learned that transfer human poses to a shared high-level invariant space. The learned CNN models are then used as invariant feature extractors from real RGB and depth frames of human action videos and the temporal variations are modelled by Fourier Temporal Pyramid. Finally, linear SVM is used for classification. Experiments on three benchmark human action datasets show that our algorithm outperforms existing methods by significant margins for RGB only and RGB-D action recognition. |
| Audience | Academic |
| Author | Liu, Jian Mian, Ajmal Akhtar, Naveed Rahmani, Hossein |
| Author_xml | – sequence: 1 givenname: Jian orcidid: 0000-0003-3258-0380 surname: Liu fullname: Liu, Jian email: jian.liu@research.uwa.edu.au organization: School of Computer Science and Software Engineering, The University of Western Australia – sequence: 2 givenname: Hossein surname: Rahmani fullname: Rahmani, Hossein organization: School of Computing and Communications, Lancaster University – sequence: 3 givenname: Naveed surname: Akhtar fullname: Akhtar, Naveed organization: School of Computer Science and Software Engineering, The University of Western Australia – sequence: 4 givenname: Ajmal surname: Mian fullname: Mian, Ajmal organization: School of Computer Science and Software Engineering, The University of Western Australia |
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| Keywords | Human action recognition CNN Cross-subject GAN Cross-view Depth sensor |
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| SubjectTerms | Algorithms Analysis Artificial Intelligence Cameras Computer Imaging Computer Science Feature extraction Human motion Human-computer interaction Image Processing and Computer Vision Invariants Learning Lighting Motion capture Moving object recognition Pattern Recognition Pattern Recognition and Graphics Synthesis Three dimensional bodies Training Vision |
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| Title | Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition |
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