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.

我们提出了 EMS，这是第一个针对单视图 3D 眉毛重建的基于学习的框架，通过将眉毛表示为一组纤维曲线并将重建转化为纤维生长问题来实现。我们设计了三个模块：RootFinder 用于定位纤维根位置，OriPredictor 用于预测 3D 空间中的方向场以指导纤维生长，FiberEnder 用于确定每个纤维生长何时停止。通过构建首个包含 400 个由艺术家手动创建的高质量 3D 合成眉毛数据集进行训练，实验证明了 EMS 在各种不同眉毛样式和长度上的有效性。

EMS：基于单视图图像的 3D 眉毛建模

EMS: 3D Eyebrow Modeling from Single-view Images

Massively multilingual sentence representation models, e.g., LASER,
SBERT-distill, and LaBSE, help significantly improve cross-lingual downstream
tasks. However, multiple training procedures, the use of a large amount of
data, or inefficient model architectures result in heavy computation to train a
new model according to our preferred languages and domains. To resolve this
issue, we introduce efficient and effective massively multilingual sentence
representation learning (EMS), using cross-lingual sentence reconstruction
(XTR) and sentence-level contrastive learning as training objectives. Compared
with related studies, the proposed model can be efficiently trained using
significantly fewer parallel sentences and GPU computation resources without
depending on large-scale pre-trained models. Empirical results show that the
proposed model significantly yields better or comparable results with regard to
bi-text mining, zero-shot cross-lingual genre classification, and sentiment
classification. Ablative analyses demonstrate the effectiveness of each
component of the proposed model. We release the codes for model training and
the EMS pre-trained model, which supports 62 languages
(this https URL).

通过跨语言重建和句子级对比学习，提出了一种有效的高效多语句子表示学习算法 EMS，不依赖于大规模预训练模型，能显著减少并行句子和 GPU 计算资源的使用，支持 62 种语言，在双语挖掘、零样本跨语言流派分类和情感分类方面，该模型显著获得更好或相当的效果。