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

TL;DR

Pixel-Face is a comprehensive high-resolution 3D face dataset with diverse subjects and annotations, enabling improved modeling and benchmarking of face reconstruction methods.

Contribution

The paper introduces Pixel-Face, a large-scale, high-resolution 3D face dataset with extensive annotations, and demonstrates its advantages for modeling and benchmarking.

Findings

Pixel-Face improves 3D face modeling accuracy.

Fine-tuning models on Pixel-Face enhances reconstruction performance.

Pixel-3DM better captures diverse face shapes and expressions.

Abstract

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.