We present Beauty3DFaceNet, the first deep convolutional neural network to predict attractiveness on 3D faces with both geometry and texture information. The proposed network can learn discriminative and complementary 2D and 3D facial features, allowing accurate attractiveness prediction for 3D faces. The main component of our network is a fusion module that fuses geometric features and texture features. We further employ a novel sampling strategy for our network based on a prior of facial landmarks, which improves the performance of learning aesthetic features from a face point cloud. Comparing to previous work, our approach takes full advantage of 3D geometry and 2D texture and does not rely on handcrafted features based on highly accurate facial characteristics such as feature points. To facilitate 3D facial attractiveness research, we also construct the first 3D face dataset ShadowFace3D, which contains 6,000 high-quality 3D faces with attractiveness labeled by human annotators. Extensive quantitative and qualitative evaluations show that Beauty3DFaceNet achieves a significant correlation with the average human ratings. This validates that a deep learning network can effectively learn and predict 3D facial attractiveness.

我们提出了Beauty3DFaceNet，这是第一个使用几何和纹理信息来预测3D面部吸引力的深度卷积神经网络。所提出的网络可以学习区分性和互补性的2D和3D面部特征，从而可以准确预测3D面部的吸引力。我们网络的主要组件是融合几何特征和纹理特征的融合模块。我们还基于先验的面部标志为网络采用了一种新颖的采样策略，从而提高了从面部点云中学习美学特征的性能。与以前的工作相比，我们的方法充分利用了3D几何和2D纹理，并且不依赖基于高度精确的面部特征（例如特征点）的手工特征。为了促进3D面部吸引力的研究，我们还构建了第一个3D人脸数据集ShadowFace3D，其中包含6,000张具有人工注释者标记的具有吸引力的3D人脸。大量的定量和定性评估表明，Beauty3DFaceNet与人类平均评分之间具有显着的相关性。这证明深度学习网络可以有效地学习和预测3D面部吸引力。