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首页> 外文期刊>Machine Vision and Applications >Three-dimensional human shape inference from silhouettes: reconstruction and validation
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Three-dimensional human shape inference from silhouettes: reconstruction and validation

机译：基于轮廓的三维人体形状推断：重构和验证

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摘要

Silhouettes are robust image features that provide considerable evidence about the three-dimensional (3D) shape of a human body. The information they provide is, however, incomplete and prior knowledge has to be integrated to reconstruction algorithms in order to obtain realistic body models. This paper presents a method that integrates both geometric and statistical priors to reconstruct the shape of a subject assuming a standardized posture from a frontal and a lateral silhouette. The method is comprised of three successive steps. First, a non-linear function that connects the silhouette appearances and the body shapes is learnt and used to create a first approximation. Then, the body shape is deformed globally along the principal directions of the population (obtained by performing principal component analysis over 359 subjects) to follow the contours of the silhouettes. Finally, the body shape is deformed locally to ensure it fits the input silhouettes as well as possible. Experimental results showed a mean absolute 3D error of 8 mm with ideal silhouettes extraction. Furthermore, experiments on body measurements (circumferences or distances between two points on the body) resulted in a mean error of 11 mm.
机译：轮廓是鲁棒的图像特征，可提供有关人体三维（3D）形状的大量证据。但是，他们提供的信息不完整，必须将先验知识集成到重建算法中，才能获得逼真的人体模型。本文提出了一种方法，该方法整合了几何先验和统计先验，以从正面和侧面轮廓采用标准姿势重建对象的形状。该方法包括三个连续的步骤。首先，学习连接轮廓外观和身体形状的非线性函数，并将其用于创建一阶近似值。然后，身体形状沿总体主方向（通过对359个对象执行主成分分析而获得）整体变形，以遵循轮廓的轮廓。最后，身体形状会局部变形以确保其尽可能适合输入轮廓。实验结果表明，理想轮廓提取的平均3D绝对误差为8mm。此外，人体测量实验（圆周或人体两点之间的距离）导致平均误差为11毫米。

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