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2023, Volume 24, Issue 10

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Frontiers of Information Technology & Electronic Engineering >> 2023, Volume 24, Issue 10 doi: 10.1631/FITEE.2200529

Path guided motion synthesis for Drosophila larvae

Affiliation(s): Research Institute of Basic Theories, Zhejiang Lab, Hangzhou 311121, China; School of Rehabilitation Sciences and Engineering, University of Health and Rehabilitation Sciences, Qingdao 266114, China; Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou 310007, China; Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Affiliated Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310058, China; 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; less

Received: 2022-10-31 Accepted: 2023-10-27 Available online: 2023-10-27
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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 of 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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