Driving 3D morphable models using shading cues
Driving 3D morphable models using shading cues
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DOI：
10.1016/j.patcog.2011.11.013
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发表时间：
2012-05
期刊：
影响因子：
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通讯作者：
Ankur Patel;W. Smith
中科院分区：
文献类型：
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作者：
Ankur Patel;W. Smith
在这篇文章中，我们展示了如何使用从明暗处理中的形状推断的表面方向信息来辅助将3D可变形模型适配到人脸图像的过程。我们考虑了模型优势的问题，并展示了如何使用阴影约束来改进可变形的模型形状估计，从而提供了超过模型的最大可能精度的可能性。我们利用这一观察结果来激励基于曲面法线误差的优化方案。这确保了图像中阴影所传达的信息得到最充分的利用。此外，我们的框架允许估计每个顶点的反照率和凹凸贴图，这些贴图不受限制位于模型的范围内。这意味着恢复的模型能够描述训练集中不存在的形状和反射现象。我们给出了重建和合成的结果，并证明了形状和反照率估计可以用于仅使用单一图库图像的光照不敏感识别。
In this paper we show how surface orientation information inferred using shape-from-shading can be used to aid the process of fitting a 3D morphable model to an image of a face. We consider the problem of model dominance and show how shading constraints can be used to refine morphable model shape estimates, offering the possibility of exceeding the maximum possible accuracy of the model. We use this observation to motivate an optimisation scheme based on surface normal error. This ensures the fullest possible use of the information conveyed by the shading in an image. Moreover, our framework allows estimation of per-vertex albedo and bump maps which are not constrained to lie within the span of the model. This means the recovered model is capable of describing shape and reflectance phenomena not present in the training set. We show reconstruction and synthesis results and demonstrate that the shape and albedo estimates can be used for illumination insensitive recognition using only a single gallery image.