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Automatic Generation and Stylization of 3D Facial Rigs

机译:3D面部钻机的自动生成和程式化

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In this paper, we present a fully automatic pipeline for generating and stylizing high geometric and textural quality facial rigs. They are automatically rigged with facial blendshapes for animation, and can be used across platforms for applications including virtual reality, augmented reality, remote collaboration, gaming and more. From a set of input facial photos, our approach is to be able to create a photorealistic, fully rigged character in less than seven minutes. The facial mesh reconstruction is based on state-of-the art photogrammetry approaches. Automatic landmarking coupled with ICP registration with regularization provide direct correspondence and registration from a given generic mesh to the acquired facial mesh. Then, using deformation transfer, existing blendshapes are transferred from the generic to the reconstructed facial mesh. The reconstructed face is then fit to the full body generic mesh. Extra geometry such as jaws, teeth and nostrils are retargeted and transferred to the character. An automatic iris color extraction algorithm is performed to colorize a separate eye texture, animated with dynamic UVs. Finally, an extra step applies a style to the photorealis-tic face to enable blending of personalized facial features into any other character. The user's face can then be adapted to any human or non-human generic mesh. A pilot user study was performed to evaluate the utility of our approach. Up to 65% of the participants were successfully able to discern the presence of one's unique facial features when the style was not too far from a humanoid shape.
机译:在本文中,我们提供了一种全自动管道,用于产生和程式化高几何和纹理质量面部钻机。它们会自动操纵有动画的面部闪打,并且可以跨平台用于应用程序,包括虚拟现实,增强现实,远程协作,游戏等。从一组输入面部照片中,我们的方法是能够在不到七分钟内创建光电型,完全装配的性格。面部网格重建基于最先进的摄影测量方法。与正则化的ICP注册耦合的自动载体提供直接对应,并从给定的通用网格注册到所获取的面部网格。然后,使用变形传输,现有的闪打从通用转移到重建的面部网格。然后将重建的面部适合全身通用网格。额外的几何形状如钳口,牙齿和鼻孔是重新靶向并转移到角色。执行自动虹膜颜色提取算法,以使单独的眼睛纹理着色,具有动态UV的动画。最后,额外的步骤将风格应用于光矩形 - TIC面,以使个性化面部特征能够将个性化的面部特征融入任何其他角色。然后,用户的脸部可以适应任何人或非人类通用网格。执行试点用户学习以评估我们方法的效用。最多65%的参与者成功地辨别出一个独特的面部特征的存在,当风格与人形形状不太太远时。

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