Abstract
3D human pose estimation is a fundamental task in computer vision. However, most of the related works focus on recovering human joint positions, which provides sparse and insufficient pose information for many applications like 3D avatar animation. Therefore, this paper presents a deep network for recovering joint angles from 3D joint positions, which learns the prior dependence between them. We test the validity and robustness of our method. We also discuss some details in designing and training the network. Our method is simple, effective and extensive. It can be combined with work of 3D human pose estimation that predict 3D joint positions from image or depth data to produce more detailed and natural poses. It builds a map between two joint sets with different numbers of joints, which provides a framework to unify multiple datasets for human pose estimation with different annotation formats.
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Acknowledgements
This work is supported by the National Key R&D Plan of China (No. 2017YFB1002804) and the National Natural Science Foundation of China (No. 61471359).
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Wu, Z., Che, W. (2019). 3D Human Pose Lifting: From Joint Position to Joint Rotation. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_22
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DOI: https://doi.org/10.1007/978-981-13-9917-6_22
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