基于路径引导的果蝇幼虫运动合成

陈俊君; 王燚军; 孙艺璇; 余益飞; 刘子奥; 龚哲峰; 郑能干

doi:10.1631/FITEE.2200529

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基于路径引导的果蝇幼虫运动合成

常规文章 | Updated：2023-10-25

- 基于路径引导的果蝇幼虫运动合成
- Path guided motion synthesis for Drosophila larvae
- 信息与电子工程前沿(英文版) 2023年24卷第10期页码：1482-1496
- Affiliations：
  
  1.Research Institute of Basic Theories, Zhejiang Lab, Hangzhou 311121, China
  2.School of Rehabilitation Sciences and Engineering, University of Health and Rehabilitation Sciences, Qingdao 266114, China
  3.Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310007, China
  4.Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Affiliated Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China
  5.NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Research and Brain-Machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
- Author bio：
  
  †E-mail: 1536779079@qq.com
  ‡Corresponding author
- Funds：
  
  the Zhejiang Lab, China(2020KB0AC02);the Zhejiang Provincial Key R&D Program, China(2022C01022;2022C01119;2021C03003);the National Natural Science Foundation of China(T2293723;61972347);the Zhejiang Provincial Natural Science Foundation, China(LR19F020005);the Fundamental Research Funds for the Central Universities, China(226-2022-00051)
- DOI：10.1631/FITEE.2200529
  中图分类号： Q811.211
- 收稿：2022-10-31，
  
  修回：2023-03-05，
  
  纸质出版：2023-10-0
- Accepted：
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陈俊君, 王燚军, 孙艺璇, 等. 基于路径引导的果蝇幼虫运动合成[J]. 信息与电子工程前沿(英文版), 2023,24(10):1482-1496.

Junjun CHEN, Yijun WANG, Yixuan SUN, et al. Path guided motion synthesis for Drosophila larvae[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(10): 1482-1496.
陈俊君, 王燚军, 孙艺璇, 等. 基于路径引导的果蝇幼虫运动合成[J]. 信息与电子工程前沿(英文版), 2023,24(10):1482-1496. DOI： 10.1631/FITEE.2200529.

Junjun CHEN, Yijun WANG, Yixuan SUN, et al. Path guided motion synthesis for Drosophila larvae[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(10): 1482-1496. DOI： 10.1631/FITEE.2200529.

摘要

软体动物身体可变形性和高自由度的特点为数学建模和运动合成带来很大挑战。受限于刚体骨骼假设或模型容量，传统解析模型和统计模型难以生成逼真和多模态的软体动物运动。本文建立一个大规模果蝇幼虫动态姿态数据集，并提出一个运动合成模型（Path2Pose），通过给定一段幼虫初始运动姿态序列和引导路径生成后续运动姿态序列。进一步地，通过循环生成的方式，Path2Pose模型可以合成长时间、多模态的果蝇幼虫运动姿态序列。运动评估实验表明，Path2Pose模型可以生成高度真实的软体动物运动，并在现有同类型模型中取得最好生成效果。本文的工作证明了深度神经网络在软体动物运动合成任务中的良好性能以及通过定制软体动物体型和引导路径生成长时间运动姿态的可行性。

Abstract

The deformability and high degree of freedom of mollusks bring challenges in mathematical modeling and synthesis of motions. Traditional analytical and statistical models are limited by either rigid skeleton assumptions or model capacity

and have difficulty in generating realistic and multi-pattern mollusk motions. In this work

we present a large-scale dynamic pose dataset of

Drosophila

larvae and propose a motion synthesis model named Path2Pose to generate a pose sequence given the initial poses and the subsequent guiding path. The Path2Pose model is further used to synthesize long pose sequences of various motion patterns through a recursive generation method. Evaluation analysis results demonstrate that our novel model synthesizes highly realistic mollusk motions and achieves state-of-the-art performance. Our work proves high performance of deep neural networks for mollusk motion synthesis and the feasibility of long pose sequence synthesis based on the customized body shape and guiding path.

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