Implementation of Single Camera Markerless Facial Motion Capture using Blendshapes

Meghana Rao Somepalli, M. Charan, S. Shruthi, Suja Palaniswamy
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引用次数: 1

Abstract

Facial motion capture is the process of digitizing the facial motion of an actor by locating several facial landmarks of the actor’s face and using the relative coordinates of these landmarks to drive the facial structure of a 3D character in software like Blender. Recent advances have enabled markerless technology to track the desired facial features from frame to frame. In this work, the input from a single front-facing camera is used and the face is located using face detection algorithms. Its output is then used to find the relative coordinates of facial landmarks like lip corners, upper eyelids, and eyebrows etc., using a facial landmark detector. To achieve comparable levels of accuracy without the depth or 3D information that would be captured from a multi-camera setup, morph targets have been used to add constraints to the animation to avoid unnatural positions of the virtual character. The distance between a referential landmark that has minimal movement and the driving landmark determines the influence of the corresponding morph target. To establish an orientation invariant landmark detection, geometric normalization and face size normalization have been deployed.
使用Blendshapes实现单相机无标记面部动作捕捉
面部动作捕捉是将演员的面部动作数字化的过程,通过定位演员面部的几个面部标志,并使用这些标志的相对坐标在Blender等软件中驱动3D角色的面部结构。最近的进步使无标记技术能够从一帧到另一帧地跟踪所需的面部特征。在这项工作中,使用来自单个前置摄像头的输入,并使用人脸检测算法定位人脸。然后,它的输出被用来找到面部标志的相对坐标,如嘴角、上眼睑和眉毛等,使用面部标志检测器。为了在没有深度或3D信息的情况下达到可比的精度水平,从多摄像机设置中捕获,变形目标已被用于添加动画约束,以避免虚拟角色的不自然位置。具有最小运动的参考地标与驱动地标之间的距离决定了相应形态目标的影响。为了建立一个方向不变的地标检测,几何归一化和人脸大小归一化被部署。
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