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.

翻译：眉毛在面部表情和外观中扮演着关键角色。尽管三维人脸数字化技术已得到充分探索，但针对三维眉毛建模的研究仍相对不足。本文提出EMS——首个基于学习的单视角三维眉毛重建框架。遵循头皮毛发重建方法，我们将眉毛表示为纤维曲线集合，并将重建问题转化为纤维生长问题。为此，我们精心设计了三个模块：RootFinder首先定位作为生长起点的纤维根部位置；OriPredictor预测三维空间中的方向场以引导纤维生长；FiberEnder则确定每根纤维的生长终止条件。其中OriPredictor直接借鉴了毛发重建方法，而考虑到眉毛与头发的差异，RootFinder和FiberEnder均为全新设计。具体而言，针对根部位置严重遮挡的挑战，我们将根部定位转化为密度图估计任务，并采用基于密度的聚类方法从预测密度图中检测根部点。对于每根纤维，生长过程从根部点出发逐步推进直至终止，每一步均根据预测方向场定义为固定长度的有向线段。为判定生长终止时机，我们设计了像素对齐的循环神经网络架构作为二值分类器，为每个生长步骤输出"停止"或"继续"的决策。为支撑所有提出网络的训练，我们构建了首个三维合成眉毛数据集，包含400个由艺术家手工制作的高质量眉毛模型。大量实验证明，所提出的EMS流水线在不同风格和长度的眉毛（从短稀疏到长浓密）上均具有有效性。