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Efficient Human Motion Transition via Hybrid Deep Neural Network and Reliable Motion Graph Mining

  • Bing Zhou
  • Xin Liu
  • Shujuan Peng
  • Bineng Zhong
  • Jixiang Du
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 771)

Abstract

Skeletal motion transition is of crucial importance to the simulation in interactive environments. In this paper, we propose a hybrid deep learning framework that allows for flexible and efficient human motion transition from motion capture (mocap) data, which optimally satisfies the diverse user-specified paths. We integrate a convolutional restricted Boltzmann machine with deep belief network to detect appropriate transition points. Subsequently, a quadruples-like data structure is exploited for motion graph building, which significantly benefits for the motion splitting and indexing. As a result, various motion clips can be well retrieved and transited fulfilling the user inputs, while preserving the smooth quality of the original data. The experiments show that the proposed transition approach performs favorably compared to the state-of-the-art competing approaches.

Keywords

Skeletal motion transition Hybrid deep learning Convolutional restricted Boltzmann machine Quadruples-like data structure 

Notes

Acknowledgment

The work described in this paper was supported by the National Science Foundation of China (Nos. 61673185, 61572205, 61673186), National Science Foundation of Fujian Province (Nos. 2015J01656, 2017J01112), Promotion Program for Young and Middle-aged Teacher in Science and Technology Research (No. ZQN-PY309), the Promotion Program for graduate student in Scientific research and innovation ability of Huaqiao University (No. 1511414012).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Bing Zhou
    • 1
    • 2
  • Xin Liu
    • 1
    • 2
  • Shujuan Peng
    • 1
    • 2
  • Bineng Zhong
    • 1
    • 2
  • Jixiang Du
    • 1
    • 2
  1. 1.Department of Computer ScienceHuaqiao UniversityXiamenChina
  2. 2.Xiamen Key Laboratory of Computer Vision and Pattern RecognitionHuaqiao UniversityXiamenChina

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