This work studies learning from a synergy process of 3D Morphable Models (3DMM) and 3D facial landmarks to predict complete 3D facial geometry, including 3D alignment, face orientation, and 3D face modeling. Our synergy process leverages a representation cycle for 3DMM parameters and 3D landmarks. 3D landmarks can be extracted and refined from face meshes built by 3DMM parameters. We next reverse the representation direction and show that predicting 3DMM parameters from sparse 3D landmarks improves the information flow. Together we create a synergy process that utilizes the relation between 3D landmarks and 3DMM parameters, and they collaboratively contribute to better performance. We extensively validate our contribution on full tasks of facial geometry prediction and show our superior and robust performance on these tasks for various scenarios. Particularly, we adopt only simple and widely-used network operations to attain fast and accurate facial geometry prediction. Codes and data: https://choyingw.github.io/works/SynergyNet/

本文研究了从3D可塑模型（3DMM）和3D面部标记的协同学习过程，以预测完整的3D面部几何，包括3D对齐，面部方向和3D面部建模。我们的协同过程利用了3DMM参数和3D标记的表示循环。3D标记可以从3DMM参数构建的面部网格中提取和精炼。接下来，我们逆转表示方向，并显示从稀疏的3D标记预测3DMM参数可以改善信息流量。总的来说，我们创建了一个利用3D标记和3DMM参数之间关系的协同过程，它们共同促成了更好的性能。我们在面部几何预测的全部任务上进行了广泛的验证，并展示了在这些任务的各种场景下我们卓越和稳健的性能。特别地，我们仅采用简单和广泛使用的网络操作来实现快速和准确的面部几何预测。

基于3DMM和3D Landmarks的协同作用用于精准的3D人脸几何模型