We propose NeuFace, a 3D face mesh pseudo annotation method on videos via neural re-parameterized optimization. Despite the huge progress in 3D face reconstruction methods, generating reliable 3D face labels for in-the-wild dynamic videos remains challenging. Using NeuFace optimization, we annotate the per-view/-frame accurate and consistent face meshes on large-scale face videos, called the NeuFace-dataset. We investigate how neural re-parameterization helps to reconstruct image-aligned facial details on 3D meshes via gradient analysis. By exploiting the naturalness and diversity of 3D faces in our dataset, we demonstrate the usefulness of our dataset for 3D face-related tasks: improving the reconstruction accuracy of an existing 3D face reconstruction model and learning 3D facial motion prior. Code and datasets will be available at https://neuface-dataset.github.

翻译：我们提出NeuFace，一种通过神经重参数化优化对视频进行3D人脸网格伪标注的方法。尽管3D人脸重建方法取得了巨大进展，但在野外动态视频中生成可靠的3D人脸标签仍然具有挑战性。通过NeuFace优化，我们在大规模人脸视频上标注了逐视角/逐帧精确且一致的人脸网格，称为NeuFace-dataset。我们通过梯度分析研究了神经重参数化如何帮助在3D网格上重建对齐图像的面部细节。利用数据集中3D人脸的自然性和多样性，我们展示了该数据集在3D人脸相关任务中的实用性：提升现有3D人脸重建模型的重建精度，并学习3D面部运动先验。代码和数据集将在https://neuface-dataset.github上公开。