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.io.

翻译：我们提出NeuFace，一种通过神经重参数化优化对视频进行三维人脸网格伪标注的方法。尽管三维人脸重建方法取得了巨大进展，为野外动态视频生成可靠的三维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每视角/每帧精确且一致的人脸网格，称为NeuFace数据集。我们通过梯度分析研究了神经重参数化如何帮助在三维网格上重建与图像对齐的面部细节。利用数据集中三维人脸的自然性和多样性，我们展示了该数据集对三维人脸相关任务的有用性：提高现有三维人脸重建模型的精度以及学习三维面部运动先验。代码和数据集将在https://neuface-dataset.github.io公开。