论文标题
实时3D面部跟踪通过级联的构图学习
Real-time 3D Facial Tracking via Cascaded Compositional Learning
论文作者
论文摘要
我们建议学习一系列全球优化的模块化增强蕨（GOMBF），以从单眼RGB摄像头求解实时3D面部跟踪的多模式面部运动回归。 GOMBF是多元回归模型的深层组成，每个回归模型都是最初训练的蕨类植物，以预测相同模态的部分运动参数，然后通过全局优化步骤将其串联在一起，以形成一个可以有效地处理整个回归目标的奇异强大蕨类植物。它可以明确应对输出变量中的方式，同时表现出提高的拟合能力和更快的学习速度，与传统的增强蕨类植物相比。通过进一步级联一系列GOMBFS（GOMBF-CASCADE）来回归面部运动参数，我们可以在各种野外视频上与最先进的方法进行竞争性跟踪性能，这与最先进的方法相比，这些方法需要更多的训练数据或具有更高的计算复杂性。它为实时3D面部跟踪提供了一种强大而优雅的解决方案，并使用一小部分培训数据，因此使其在现实世界应用中更加实用。
We propose to learn a cascade of globally-optimized modular boosted ferns (GoMBF) to solve multi-modal facial motion regression for real-time 3D facial tracking from a monocular RGB camera. GoMBF is a deep composition of multiple regression models with each is a boosted ferns initially trained to predict partial motion parameters of the same modality, and then concatenated together via a global optimization step to form a singular strong boosted ferns that can effectively handle the whole regression target. It can explicitly cope with the modality variety in output variables, while manifesting increased fitting power and a faster learning speed comparing against the conventional boosted ferns. By further cascading a sequence of GoMBFs (GoMBF-Cascade) to regress facial motion parameters, we achieve competitive tracking performance on a variety of in-the-wild videos comparing to the state-of-the-art methods, which require much more training data or have higher computational complexity. It provides a robust and highly elegant solution to real-time 3D facial tracking using a small set of training data and hence makes it more practical in real-world applications.
