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Real-Time 3D Facial Tracking via Cascaded Compositional Learning by Jianwen Lou; Xiaoxu Cai; Junyu Dong; Hui Yu is a Computer Science article available to read on EtoBox.

What is Real-Time 3D Facial Tracking via Cascaded Compositional Learning about?

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 either have higher computational complexity or require much more training data. 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. We further deeply

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Author

Jianwen Lou; Xiaoxu Cai; Junyu Dong; Hui Yu

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)