To enable real-time, person-independent 3D registration from 2D video, we developed a 3D cascade regression approach in which facial landmarks remain invariant across pose over a range of approximately 60°. From a single 2D image of a person's face, a dense 3D shape is registered in real time for each frame. The algorithm utilizes a fast cascade regression framework trained on high-resolution 3D face-scans of posed and spontaneous emotion expression. The algorithm first estimates the location of a dense set of landmarks and their visibility, then reconstructs face shapes by fitting a part-based 3D model. Because no assumptions are required about illumination or surface properties, the method can be applied to a wide range of imaging conditions that include 2D video and uncalibrated multi-view video. The method has been validated in a battery of experiments that evaluate its precision of 3D reconstruction, extension to multi-view reconstruction, temporal integration for videos and 3D head-pose estimation. Experimental findings strongly support the validity of real-time, 3D registration and reconstruction from 2D video. The software is available online at http://zface.org.

为了实现2D视频的实时，独立于人的3D注册，我们开发了3D级联回归方法，其中面部标志在整个姿势中在大约60°的范围内保持不变。从人脸的单个2D图像中，每帧实时记录密集的3D形状。该算法利用对姿势和自发情绪表达的高分辨率3D面部扫描训练的快速级联回归框架。该算法首先估计一组密集的地标的位置及其可见性，然后通过拟合基于零件的3D模型来重建面部形状。因为不需要关于照明或表面特性的任何假设，所以该方法可以应用于包括2D视频和未校准的多视图视频的广泛成像条件。该方法已在一系列实验中得到验证，这些实验评估其3D重建的精度，扩展到多视图重建，视频的时间积分和3D头部姿势估计。实验结果强烈支持实时，3D配准和从2D视频重建的有效性。该软件可从http://zface.org在线获得。