  3D face reconstruction is a fundamental task that can facilitate numerous
applications such as robust facial analysis and augmented reality. It is also a
challenging task due to the lack of high-quality datasets that can fuel current
deep learning-based methods. However, existing datasets are limited in
quantity, realisticity and diversity. To circumvent these hurdles, we introduce
Pixel-Face, a large-scale, high-resolution and diverse 3D face dataset with
massive annotations. Specifically, Pixel-Face contains 855 subjects aging from
18 to 80. Each subject has more than 20 samples with various expressions. Each
sample is composed of high-resolution multi-view RGB images and 3D meshes with
various expressions. Moreover, we collect precise landmarks annotation and 3D
registration result for each data. To demonstrate the advantages of Pixel-Face,
we re-parameterize the 3D Morphable Model (3DMM) into Pixel-3DM using the
collected data. We show that the obtained Pixel-3DM is better in modeling a
wide range of face shapes and expressions. We also carefully benchmark existing
3D face reconstruction methods on our dataset. Moreover, Pixel-Face serves as
an effective training source. We observe that the performance of current face
reconstruction models significantly improves both on existing benchmarks and
Pixel-Face after being fine-tuned using our newly collected data. Extensive
experiments demonstrate the effectiveness of Pixel-3DM and the usefulness of
Pixel-Face.
