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.
@article{arxiv.2101.12276,
title = {A causal convolutional neural network for multi-subject motion modeling and generation},
author = {Shuaiying Hou and Congyi Wang and Wenlin Zhuang and Yu Chen and Yangang Wang and Hujun Bao and Jinxiang Chai and Weiwei Xu},
journal= {arXiv preprint arXiv:2101.12276},
year = {2023}
}
Comments
This preprint has not undergone peer review (when applicable) or any post-submission improvements or corrections. The Version of Record of this article is published in Computational Visual Media, and is available online at https://doi.org/10.1007/s41095-022-0307-3