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3D face reconstruction is a fundamental task that can facilitate numerousapplications such as robust facial analysis and augmented reality. It is also achallenging task due to the lack of high-quality datasets that can fuel currentdeep learning-based methods. However, existing datasets are limited inquantity, realisticity and diversity. To circumvent these hurdles, we introducePixel-Face, a large-scale, high-resolution and diverse 3D face dataset withmassive annotations. Specifically, Pixel-Face contains 855 subjects aging from18 to 80. Each subject has more than 20 samples with various expressions. Eachsample is composed of high-resolution multi-view RGB images and 3D meshes withvarious expressions. Moreover, we collect precise landmarks annotation and 3Dregistration result for each data. To demonstrate the advantages of Pixel-Face,we re-parameterize the 3D Morphable Model (3DMM) into Pixel-3DM using thecollected data. We show that the obtained Pixel-3DM is better in modeling awide range of face shapes and expressions. We also carefully benchmark existing3D face reconstruction methods on our dataset. Moreover, Pixel-Face serves asan effective training source. We observe that the performance of current facereconstruction models significantly improves both on existing benchmarks andPixel-Face after being fine-tuned using our newly collected data. Extensiveexperiments demonstrate the effectiveness of Pixel-3DM and the usefulness ofPixel-Face.

Pixel-Face: A Large-Scale, High-Resolution Benchmark for 3D Face Reconstruction