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Research Article | Open Access

A causal convolutional neural network for multi-subject motion modeling and generation

State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310058, China
Xmov, Shanghai 200030, China
School of Automation, Southeast University, Nanjing 210096, China
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Abstract

Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the motion of different subjects, such as the influence of skeleton scale variation on motion style. Moreover, after fine-tuning the network using a small motion dataset for a novel skeleton that is not included in the training dataset, it is able to synthesize high-quality motions with a personalized style for the novel skeleton. The experimental results demonstrate that our network can model the intrinsic characteristics of motions well and can be applied to various motion modeling and synthesis tasks.

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Keywords

deep learningoptimizationmotion generationmotion denoisingmotion control

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Computational Visual Media
Volume 10 Issue 1,
February 2024
Pages 45-59
DOI: 10.1007/s41095-022-0307-3

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Hou S, Wang C, Zhuang W, et al. A causal convolutional neural network for multi-subject motion modeling and generation. Computational Visual Media, 2024, 10(1): 45-59. https://doi.org/10.1007/s41095-022-0307-3

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Received: 25 May 2022
Accepted: 02 August 2022
Published: 30 November 2023
© The Author(s) 2023.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduc-tion in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

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