Many recent works have reconstructed distinctive 3D face shapes by aggregating shape parameters of the same identity and separating those of different people based on parametric models (e.g., 3D morphable models (3DMMs)). However, despite the high accuracy in the face recognition task using these shape parameters, the visual discrimination of face shapes reconstructed from those parameters is unsatisfactory. The following research question has not been answered in previous works: Do discriminative shape parameters guarantee visual discrimination in represented 3D face shapes? This paper analyzes the relationship between shape parameters and reconstructed shape geometry and proposes a novel shape identity-aware regularization(SIR) loss for shape parameters, aiming at increasing discriminability in both the shape parameter and shape geometry domains. Moreover, to cope with the lack of training data containing both landmark and identity annotations, we propose a network structure and an associated training strategy to leverage mixed data containing either identity or landmark labels. We compare our method with existing methods in terms of the reconstruction error, visual distinguishability, and face recognition accuracy of the shape parameters. Experimental results show that our method outperforms the state-of-the-art methods.

中文翻译：

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基于3D变形模型重建可识别的3D人脸形状

许多新近的作品通过聚合相同身份的形状参数并基于参数模型（例如3D可变形模型（3DMM））来分离不同人的形状参数，从而重构了独特的3D面部形状。然而，尽管在使用这些形状参数的面部识别任务中具有很高的准确性，但是从这些参数重建的面部形状的视觉辨认还是不能令人满意的。以下研究问题在以前的工作中尚未得到回答：区分形状参数是否可以保证在表示的3D面部形状中具有视觉辨别力？本文分析了形状参数与重构形状几何之间的关系，并提出了一种新颖的形状参数识别意识正则化（SIR）损失，旨在提高形状参数和形状几何域中的可分辨性。此外，为了解决缺乏同时包含地标和身份注释的训练数据的问题，我们提出了一种网络结构和相关的训练策略，以利用包含身份或地标标签的混合数据。我们将我们的方法与现有方法在形状参数的重建误差，视觉可分辨性和面部识别准确性方面进行了比较。实验结果表明，我们的方法优于最新方法。以及形状参数的人脸识别精度。实验结果表明，我们的方法优于最新方法。以及形状参数的人脸识别精度。实验结果表明，我们的方法优于最新方法。