Cascaded Regressor based 3D Face Reconstruction from a Single Arbitrary View Image
Cascaded Regressor based 3D Face Reconstruction from a Single Arbitrary View Image
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发表时间：
2015-09
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通讯作者：
Feng Liu;Dan Zeng;Jing Li;Qijun Zhao
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作者：
Feng Liu;Dan Zeng;Jing Li;Qijun Zhao
最先进的方法通过将3D面部模型拟合到输入图像或直接学习二维（2D）图像和三维面部之间的映射函数，从单个图像重建三维（3D）面部形状。然而，由于昂贵的在线优化或正面人脸图像的要求，它们通常难以在实际应用中使用。本文将三维人脸重建问题作为一个回归问题而不是模型拟合问题来研究。给定一个输入的人脸图像以及一些预先定义的人脸标记，基于输入标记与从重建的3D人脸中获得的标记之间的偏差，通过级联回归计算对初始3D人脸形状的一系列形状调整。级联回归量从一组三维人脸及其对应的二维人脸图像中离线学习。通过将大视角下不可见的地标作为缺失数据处理，该方法可以用相同的回归量统一处理任意视角的人脸图像。在BFM和Bosphorus数据库上的实验表明，与现有方法相比，该方法可以更高效、更准确地从任意视图图像中重建三维人脸。
State-of-the-art methods reconstruct three-dimensional (3D) face shapes from a single image by fitting 3D face models to input images or by directly learning mapping functions between two-dimensional (2D) images and 3D faces. However, they are often difficult to use in real-world applications due to expensive online optimization or to the requirement of frontal face images. This paper approaches the 3D face reconstruction problem as a regression problem rather than a model fitting problem. Given an input face image along with some pre-defined facial landmarks on it, a series of shape adjustments to the initial 3D face shape are computed through cascaded regressors based on the deviations between the input landmarks and the landmarks obtained from the reconstructed 3D faces. The cascaded regressors are offline learned from a set of 3D faces and their corresponding 2D face images in various views. By treating the landmarks that are invisible in large view angles as missing data, the proposed method can handle arbitrary view face images in a unified way with the same regressors. Experiments on the BFM and Bosphorus databases demonstrate that the proposed method can reconstruct 3D faces from arbitrary view images more efficiently and more accurately than existing methods.