Fast Adaptive Character Animation Synthesis Based on Greedy Algorithm

Yanqiu Zhu, Qixing Chen, Jing Liu, Xiaoying Tian and Wei Wang

Complexity, 2021, vol. 2021, 1-11

Abstract: On the premise of ensuring the animation effect and real-time performance, it is of great significance and value for large-scale group character animation synthesis how to reduce the disaster coincidence degree among various models of fast adaptive character animation synthesis. The realization method of object-oriented finite state machine is studied in detail. Finite state machine (FSM) is an efficient behavior modeling method, which can describe the behavioral decisions of fast adaptive character animation synthesis in a complex virtual environment. Based on the implementation defects of the finite state machine in the traditional structure, using object-oriented thinking, combined with the state design mode, we further studied a finite state machine implementation method based on object-oriented technology. This achieves code reuse and simple program maintenance. The effect is extensible and effectively overcomes the shortcomings of traditional character animation synthesis. Secondly, the multipath matching tracking algorithm of the greedy algorithm is studied to generate multiple candidate sets through multiple paths, and finally, the candidate set with the minimum residual error is selected as the estimated support set, so as to improve the reconstruction performance. Further, based on the idea of multipath, using the regularization method of the ROMP algorithm, the regularized multipath matching tracking RMSP algorithm is proposed. It uses the regularized subset method to generate multiple paths and chooses the path with the fastest residual reduction as the support set of this iteration. The simulation results show that the RMSP algorithm has better reconstruction performance than the SP algorithm.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6685861

DOI: 10.1155/2021/6685861

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