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A self-adapting hierarchical actions and structures joint optimization framework for automatic design of robotic and animation skeletons

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Abstract

Skeleton designs are widely seen in the robotics industry and multimedia applications such as animated films and computer games. The design of skeletons is mental-labor intensive, especially axes directions of joints are difficult even for the most experienced designers to select. In the existing works, there are auto creation of skeletons from meshes, and skeleton axes optimizations from a predefined set of actions. In this work, we extend automatic construction of skeletons by proposing skeleton axes design from an objective task. A two-layered framework is proposed to optimize randomly initialized axes based on auto generated actions. First, the limit of skeleton scales that can be automatically designed is discussed. Second, a self-adaptive actions discretizer implemented by neural networks is proposed to reduce the optimization complexity. Third, actions and axes are scored by physics engine simulation, and optimizations on the score generate axes that have the best performance. Experiments include robotic designs and an animation application. Comparisons show that the proposed framework outperforms other mainstream optimizers both in speed and effectiveness.

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Funding

This study was funded by Science Foundation of China (Grant Number 61602421).

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Authors and Affiliations

  1. College of Information Science and Engineering, Jishou University, Jishou, China

    Zhiyang Xiang

  2. School of Mechanical and Electrical Engineering, Central South University, Changsha, China

    Chuang Xiang

  3. The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong

    Tong Li

  4. Center for Interdisciplinary Information Sciences, Zhengzhou University, Zhengzhou, China

    Yibo Guo

Authors
  1. Zhiyang Xiang
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  2. Chuang Xiang
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  3. Tong Li
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  4. Yibo Guo
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Correspondence to Zhiyang Xiang.

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Communicated by V. Loia.

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Xiang, Z., Xiang, C., Li, T. et al. A self-adapting hierarchical actions and structures joint optimization framework for automatic design of robotic and animation skeletons. Soft Comput 25, 263–276 (2021). https://doi.org/10.1007/s00500-020-05139-5

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