【摘要】 为提升单张图片三维重建的效率、提高重建的精准度,提出了一种基于单张图片和统计模型的三维人脸重建技术。首先基于智能行为理解组(intelligent behaviour understanding group,IBUG),采用监督下降算法(SDM)进行训练来建立二维人脸特征点参数模型;再应用该参数模型对给定图片进行特征点提取;然后基于北京工业大学3D数据库(BJUT-3D)进行三维人脸统计模型的构建;最后,以三维人脸统计模型为基础,使用学习因子自适应梯度下降法对能量函数进行迭代优化,得到统计模型参数化向量,使用该向量来调整统计模型,实现与给定图片相匹配的三维人脸模型构建。实验证明,与现有的方法相比,本文方法重建出的三维人脸模型具有更高的精确度和自适应度。更多还原

【Abstract】 3D face model reconstruction based on a single frontal photo is widely used in practical applications. In order to improve the efficiency and accuracy of 3D reconstruction based on single frontal image, this work proposes an effective approach to reconstruct a 3D face model based on a single image and a statistical model. First, we train with a SDM algorithm to establish a two-dimensional human face feature point parameter model based on the IBUG two-dimensional face database. The parameter model is used to extract feature points from a given image. A 3D face statistics model is then constructed based on the BJUT-3D database. In order to determine the parametric vector of the statistical model, we optimize the energy function with a learning factor adaptive gradient descent method iteratively based on the 3D face statistics model. Finally, we adjust the statistical model to reconstruct the 3D face model of the given image. The experimental results show that our face reconstruction method has higher accuracy and adaptability than representative commonly used algorithms.更多还原