Face modeling and animation is one of the most challenging problems in Computer Graphics. In this paper,we describe our study of face modeling and animation,especially of three-dimensional model-based facial animation...Face modeling and animation is one of the most challenging problems in Computer Graphics. In this paper,we describe our study of face modeling and animation,especially of three-dimensional model-based facial animation.Our study includes the following aspects: developing a face model editor; realizing face model calibration; generatinga realistic face image; developing a MPEG-4 compliant facial animation system; developing two speech animation sys-tems,one is based on KD2000,the other is based on SAPI5.0.展开更多
双向长短时记忆(bidirectional lorg short term memory,BLSTM)是一种特殊的递归神经网络(recurrent neural network,RNN),能够有效地对语音的长时上下文进行建模。该文提出一种基于深度BLSTM的语音驱动面部动画合成方法,利用说话人的...双向长短时记忆(bidirectional lorg short term memory,BLSTM)是一种特殊的递归神经网络(recurrent neural network,RNN),能够有效地对语音的长时上下文进行建模。该文提出一种基于深度BLSTM的语音驱动面部动画合成方法,利用说话人的音视频双模态信息训练BLSTM-RNN神经网络,采用主动外观模型(active appearance model,AAM)对人脸图像进行建模,将AAM模型参数作为网络输出,研究网络结构和不同语音特征输入对动画合成效果的影响。基于LIPS2008标准评测库的实验结果表明:具有BLSTM层的网络效果明显优于前向网络的,基于BLSTM-前向-BLSTM 256节点(BFB256)的三层模型结构的效果最佳,FBank、基频和能量组合可以进一步提升动画合成效果。展开更多
文摘Face modeling and animation is one of the most challenging problems in Computer Graphics. In this paper,we describe our study of face modeling and animation,especially of three-dimensional model-based facial animation.Our study includes the following aspects: developing a face model editor; realizing face model calibration; generatinga realistic face image; developing a MPEG-4 compliant facial animation system; developing two speech animation sys-tems,one is based on KD2000,the other is based on SAPI5.0.
文摘双向长短时记忆(bidirectional lorg short term memory,BLSTM)是一种特殊的递归神经网络(recurrent neural network,RNN),能够有效地对语音的长时上下文进行建模。该文提出一种基于深度BLSTM的语音驱动面部动画合成方法,利用说话人的音视频双模态信息训练BLSTM-RNN神经网络,采用主动外观模型(active appearance model,AAM)对人脸图像进行建模,将AAM模型参数作为网络输出,研究网络结构和不同语音特征输入对动画合成效果的影响。基于LIPS2008标准评测库的实验结果表明:具有BLSTM层的网络效果明显优于前向网络的,基于BLSTM-前向-BLSTM 256节点(BFB256)的三层模型结构的效果最佳,FBank、基频和能量组合可以进一步提升动画合成效果。
