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基于自适应回归模型和视频面部跟踪的三维动画表情驱动研究

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随着科学技术的飞速发展,三维人脸识别和表情驱动也得到更多的应用.但由于该技术的设备要求较高,运用的成本也随之增加,如何在保证完成三维动画表情驱动的前提下降低成本,推广其实际的应用是一个重要的课题.研究针对这些问题构建了融合自适应回归模型和视频面部跟踪的三维动画表情驱动模型.首先利用三维形状回归模型、局部约束模型与模型方法进行对比,然后将它们的运行时间、准确程度进行分析.最后计算得出单帧运行消耗时间、模型准确性,验证了模型方法在三维动画表情驱动中的可行性.
Research on 3D Animation Expression Driving Based on Adaptive Regression Model and Video Facial Tracking
With the rapid development of science and technology,three-dimensional face recog-nition and expression drive are also being used more often.However,since the equipment requirements of this technology are high and the cost of its application increases,how to reduce the cost and promote its practical application while ensuring the completion of 3D animated expression driving is an important topic.The research addresses these issues by constructing a 3D animated expression-driven model that incorporates an adaptive regression model and video facial tracking.Firstly,the 3D shape regression model and local constraint model are compared with the model method,and then their running time and accuracy are analyzed.Finally,the single frame running consumption time and model accuracy are calcu-lated to verify the feasibility of the model method in 3D animation expression driving.

adaptive regression modelemotion drivenfacial tracking3D animation

米娜

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安徽绿海商务职业学院科学与艺术学院,安徽合肥 230006

自适应回归模型 表情驱动 面部跟踪 三维动画

安徽省高等科学研究项目

2022AH052913

2024

佳木斯大学学报(自然科学版)
佳木斯大学

佳木斯大学学报(自然科学版)

影响因子:0.159
ISSN:1008-1402
年,卷(期):2024.42(1)
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