A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization

We assess the fidelity of our dataset by investigating the cross-view vertex distance and the 3D motion stability index. We demonstrate that our dataset contains more spatio-temporally consistent and accurate 3D meshes than the competing datasets built with strong baseline methods. To demonstrate the potential of our dataset, we present two applications: (1) improving the accuracy of a face reconstruction model and (2) learning a generative 3D facial motion prior. These applications highlight that NeuFace-dataset can be further used in diverse applications demanding high-quality and large-scale 3D face meshes. We summarize our main contributions as follows: NeuFace, an optimization method for reconstructing accurate and spatio-temporally consistent 3D face meshes on videos via neural re-parameterization. NeuFace-dataset, the first large-scale 3D face mesh pseudo-labels constructed by curating existing large-scale 2D face video datasets with our method. Demonstrating the benefits of NeuFace-dataset: (1) improve the accuracy of off-the-shelf face mesh regressors, (2) learn 3D facial motion prior for long-term face motion generation.