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3D Face Expression Estimation and Generation from 2D Image Based on a Physically Constraint Model

机译:基于物理约束模型的2D图像3D人脸表情估计和生成

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摘要

Muscle based face image synthesis is one of the most realistic approaches to the realization of a life-like agent in computers. A facial muscle model is composed of facial tissue elements and simulated muscles. In this model, forces are calculated effecting a facial tissue element by contraction of each muscle string, so the combination of each muscle contracting force decides a specific facial expression. This muscle parameter is determined on a trial and error basis by comparing the sample photograph and a generated image using our Muscle-Editor to generate a specific face image. In this paper, we propose the strategy of automatic estimation of facial muscle parameters from 2D markers'movements located on a face using a neural network. This corresponds to the non-realtime 3D facial motion capturing from 2D camera image under the physics based condition.
机译:基于肌肉的面部图像合成是在计算机中实现逼真的代理的最现实方法之一。面部肌肉模型由面部组织元素和模拟的肌肉组成。在此模型中,通过计算每个肌肉弦的收缩力来影响面部组织元素,因此每个肌肉收缩力的组合决定了特定的面部表情。通过使用我们的Muscle-Editor将样本照片与生成的图像进行比较以生成特定的面部图像,在反复试验的基础上确定该肌肉参数。在本文中,我们提出了使用神经网络从位于面部的二维标记运动自动估计面部肌肉参数的策略。这对应于在基于物理学的条件下从2D摄像机图像进行的非实时3D面部动作捕捉。

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