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Spatial-temporal modeling for prediction of stylized human motion

文献类型：期刊论文

作者Zhong, Chongyang1,2; Hu, Lei1,2; Xia, Shihong1,2
刊名NEUROCOMPUTING
出版日期2022-10-28
卷号511页码:34-42
关键词stylized motion transformer human motion prediction spatial-temporal modeling constant variance GMM
ISSN号0925-2312
DOI10.1016/j.neucom.2022.08.075
英文摘要Human motion prediction refers to forecasting human motion in the future given a past motion sequence, which has significant applications in human tracking, automatic motion generation, autonomous driving, human-robotics interaction, etc. Previous works usually used RNN-based methods, focusing on modeling the temporal dynamics of human motion, which have made great effort on content motions. However, it is unclear for their performance on stylized motion, which is with more expressive emotions and states of the human motion. Different styles within the same motion type have similar motion patterns but also subtle variances. This makes it difficult to be predicted. The main idea of this paper is to learn the spatial characteristic of stylized motion and combine it with the temporal dynamics to achieve accurate prediction. We adopt a transformer-based style encoder to learn the motion representation in the pose space and then maps it to the latent space modeled by the constant variance Gaussian mixture model; meanwhile, we use the hierarchical multi-scale RNN as a temporal encoder to capture the temporal dynamics of human motion; finally, we feed the spatial and temporal features into the prediction decoder to predict the next frame. Our experiments on the Human 3.6 M and Stylized MotionDatasets demonstrate that our model has comparable prediction performance with the state-of-the-art motion prediction works on Human 3.6 M and outperforms previous works on stylized human motion prediction. (C) 2022 Elsevier B.V. All rights reserved.
资助项目National Key R&D Program Science and Technology Winter Olympics Key Special Project[2020YFF0304701] ; National Natural Science Foundation of China[61173055]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000871948700003
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/19758]  
专题中国科学院计算技术研究所期刊论文
通讯作者Xia, Shihong
作者单位1.Univ Chinese Acad Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhong, Chongyang,Hu, Lei,Xia, Shihong. Spatial-temporal modeling for prediction of stylized human motion[J]. NEUROCOMPUTING,2022,511:34-42.
APA Zhong, Chongyang,Hu, Lei,&Xia, Shihong.(2022).Spatial-temporal modeling for prediction of stylized human motion.NEUROCOMPUTING,511,34-42.
MLA Zhong, Chongyang,et al."Spatial-temporal modeling for prediction of stylized human motion".NEUROCOMPUTING 511(2022):34-42.

入库方式： OAI收割

来源：计算技术研究所

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