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

arXiv: https://arxiv.org/abs/2008.12444 · HTML: https://mlsanity.com/paper/2008.12444/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 2 of 2)

- [REALY: Rethinking the Evaluation of 3D Face Reconstruction](https://mlsanity.com/paper/2203.09729/) (2022)
- [From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion](https://mlsanity.com/paper/2204.03842/) (2022)

## Abstract (arXiv)

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.