Dense 3D facial motion capture from only monocular in-the-wild pairs of RGB
images is a highly challenging problem with numerous applications, ranging from
facial expression recognition to facial reenactment. In this work, we propose
DeepFaceFlow, a robust, fast, and highly-accurate framework for the dense
estimation of 3D non-rigid facial flow between pairs of monocular images. Our
DeepFaceFlow framework was trained and tested on two very large-scale facial
video datasets, one of them of our own collection and annotation, with the aid
of occlusion-aware and 3D-based loss function. We conduct comprehensive
experiments probing different aspects of our approach and demonstrating its
improved performance against state-of-the-art flow and 3D reconstruction
methods. Furthermore, we incorporate our framework in a full-head
state-of-the-art facial video synthesis method and demonstrate the ability of
our method in better representing and capturing the facial dynamics, resulting
in a highly-realistic facial video synthesis. Given registered pairs of images,
our framework generates 3D flow maps at ~60 fps.

DeepFaceFlow 是一种基于 3D 的深度学习框架，用于快速准确地从单目图像中捕捉面部运动，并且可以应用于面部表情识别等领域，同时结合了 occlusion-aware 和 3D-based 损失函数进行训练和测试，方法优于现有的流和 3D 方法，还能作为完整的头部面部视频合成中的一部分。