  Eyebrows play a critical role in facial expression and appearance. Although
the 3D digitization of faces is well explored, less attention has been drawn to
3D eyebrow modeling. In this work, we propose EMS, the first learning-based
framework for single-view 3D eyebrow reconstruction. Following the methods of
scalp hair reconstruction, we also represent the eyebrow as a set of fiber
curves and convert the reconstruction to fibers growing problem. Three modules
are then carefully designed: RootFinder firstly localizes the fiber root
positions which indicates where to grow; OriPredictor predicts an orientation
field in the 3D space to guide the growing of fibers; FiberEnder is designed to
determine when to stop the growth of each fiber. Our OriPredictor is directly
borrowing the method used in hair reconstruction. Considering the differences
between hair and eyebrows, both RootFinder and FiberEnder are newly proposed.
Specifically, to cope with the challenge that the root location is severely
occluded, we formulate root localization as a density map estimation task.
Given the predicted density map, a density-based clustering method is further
used for finding the roots. For each fiber, the growth starts from the root
point and moves step by step until the ending, where each step is defined as an
oriented line with a constant length according to the predicted orientation
field. To determine when to end, a pixel-aligned RNN architecture is designed
to form a binary classifier, which outputs stop or not for each growing step.
To support the training of all proposed networks, we build the first 3D
synthetic eyebrow dataset that contains 400 high-quality eyebrow models
manually created by artists. Extensive experiments have demonstrated the
effectiveness of the proposed EMS pipeline on a variety of different eyebrow
styles and lengths, ranging from short and sparse to long bushy eyebrows.
