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Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control

文献类型：期刊论文

作者Wang, Zhiyong1,3; Chai, Jinxiang2; Xia, Shihong1,3
刊名IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
出版日期2021
卷号27期号:1页码:14-28
关键词Hidden Markov models Data models Training Generators Mathematical model Recurrent neural networks Animation Deep learning adversarial training human motion modeling synthesis and control
ISSN号1077-2626
DOI10.1109/TVCG.2019.2938520
英文摘要This paper introduces a new generative deep learning network for human motion synthesis and control. Our key idea is to combine recurrent neural networks (RNNs) and adversarial training for human motion modeling. We first describe an efficient method for training an RNN model from prerecorded motion data. We implement RNNs with long short-term memory (LSTM) cells because they are capable of addressing the nonlinear dynamics and long term temporal dependencies present in human motions. Next, we train a refiner network using an adversarial loss, similar to generative adversarial networks (GANs), such that refined motion sequences are indistinguishable from real mocap data using a discriminative network. The resulting model is appealing for motion synthesis and control because it is compact, contact-aware, and can generate an infinite number of naturally looking motions with infinite lengths. Our experiments show that motions generated by our deep learning model are always highly realistic and comparable to high-quality motion capture data. We demonstrate the power and effectiveness of our models by exploring a variety of applications, ranging from random motion synthesis, online/offline motion control, and motion filtering. We show the superiority of our generative model by comparison against baseline models.
资助项目National Natural Science Foundation of China[61772499] ; Natural Science Foundation of Beijing Municipality[L182052]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000594242000002
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/16502]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Chai, Jinxiang; Xia, Shihong
作者单位1.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China
2.Texas A&M Univ, Uvalde, TX 78801 USA
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Zhiyong,Chai, Jinxiang,Xia, Shihong. Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control[J]. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,2021,27(1):14-28.
APA Wang, Zhiyong,Chai, Jinxiang,&Xia, Shihong.(2021).Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control.IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS,27(1),14-28.
MLA Wang, Zhiyong,et al."Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control".IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 27.1(2021):14-28.

入库方式： OAI收割

来源：计算技术研究所

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