A Motion Generation Method Based on CNN and Pose Adaptation
A 3D human motion model generation method based on CNN and pose adaptation is proposed to address the issues of low accuracy and poor stability of data-driven human motion model generation network.Firstly,to improve the feature extraction performance of the encoder in the human motion model generation network for input motion sequences,a CNN convolutional network is introduced into the variational autoencoder to better extract motion features from motion data.Secondly,a convolutional network is introduced into the motion generation network to achieve the adaptive mapping of motion features,path parameters,and generation model.Finally,a 3D human motion model is generated along the set path.The experimental results show that compared with CAE algorithm and CAE improved algorithm,the proposed method can effectively reduce the reconstruction losses and generate a more accurate and natural 3D human motion model.
convolutional neural networkposture adaptationmotion model generationfeature extraction
NETL
NSTL
万方数据
