基于神经重参数化优化的大规模3D人脸网格视频数据集
A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization
AI总结：
针对野外动态视频难生成可靠3D人脸标签的问题，研究提出NeuFace神经重参数化优化方法，构建大规模标注的NeuFace-dataset，可提升3D人脸重建精度并助力人脸运动先验学习。
摘要：
我们提出NeuFace，一种通过神经重参数化优化实现的视频3D人脸网格伪标注方法。尽管3D人脸重建方法已取得巨大进展，但为野外动态视频生成可靠的3D人脸标签仍然具有挑战性。借助NeuFace优化，我们在大规模人脸视频上标注了逐视角/逐帧的准确且一致的人脸网格，将其被称为NeuFace-dataset（NeuFace数据集）。我们通过梯度分析探究了神经重参数化如何帮助在3D网格上重建与图像对齐的面部细节。利用我们数据集中3D人脸的自然性和多样性，我们证明了该数据集对3D人脸相关任务的实用性：提升现有3D人脸重建模型的重建精度，以及学习3D人脸运动先验。代码和数据集将在https://neuface-dataset.github.io发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
