# Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild

arXiv: https://arxiv.org/abs/1803.05536 · HTML: https://mlsanity.com/paper/1803.05536/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2018.

## Built on by (most cited first, 22 of 22)

- [EMOCA: Emotion Driven Monocular Face Capture and Animation](https://mlsanity.com/paper/2204.11312/) (2022)
- [Towards Metrical Reconstruction of Human Faces](https://mlsanity.com/paper/2204.06607/) (2022)
- [PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images](https://mlsanity.com/paper/2207.06400/) (2022)
- [MogaNet: Multi-order Gated Aggregation Network](https://mlsanity.com/paper/2211.03295/) (2022)
- [FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction](https://mlsanity.com/paper/2111.01082/) (2021)
- [REALY: Rethinking the Evaluation of 3D Face Reconstruction](https://mlsanity.com/paper/2203.09729/) (2022)
- [Towards Realistic Generative 3D Face Models](https://mlsanity.com/paper/2304.12483/) (2023)
- [Pixel3DMM: Versatile Screen-Space Priors for Single-Image 3D Face Reconstruction](https://mlsanity.com/paper/2505.00615/) (2025)
- [TEASER: Token Enhanced Spatial Modeling for Expressions Reconstruction](https://mlsanity.com/paper/2502.10982/) (2025)
- [A Perceptual Shape Loss for Monocular 3D Face Reconstruction](https://mlsanity.com/paper/2310.19580/) (2023)
- [Are 3D Face Shapes Expressive Enough for Recognising Continuous Emotions and Action Unit Intensities?](https://mlsanity.com/paper/2207.01113/) (2022)
- [Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation](https://mlsanity.com/paper/2207.09019/) (2022)
- [SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines](https://mlsanity.com/paper/2301.08153/) (2023)
- [End-to-End Latency Optimization of Multi-view 3D Reconstruction for Disaster Response](https://mlsanity.com/paper/2304.01488/) (2023)
- [FuRPE: Learning Full-body Reconstruction from Part Experts](https://mlsanity.com/paper/2212.00731/) (2022)
- [Application of Self-Supervised Learning to MICA Model for Reconstructing Imperfect 3D Facial Structures](https://mlsanity.com/paper/2304.04060/) (2023)
- [A survey and classification of face alignment methods based on face models](https://mlsanity.com/paper/2311.03082/) (2023)
- [3D Face Reconstruction Error Decomposed: A Modular Benchmark for Fair and Fast Method Evaluation](https://mlsanity.com/paper/2505.18025/) (2025)
- [3D Face Reconstruction Using A Spectral-Based Graph Convolution Encoder](https://mlsanity.com/paper/2403.05218/) (2024)
- [OFER: Occluded Face Expression Reconstruction](https://mlsanity.com/paper/2410.21629/) (2024)
- [Discovering Geometric Biases in 3D Face Reconstruction: A Curvature-Aware Spectral Framework for Fairness Evaluation](https://mlsanity.com/paper/2607.07486/) (2026)
- [GRAPE: Graduated Routing for Articulated Portrait mesh Estimation](https://mlsanity.com/paper/2607.23657/) (2026)

## Abstract (arXiv)

This paper investigates the evaluation of dense 3D face reconstruction from a single 2D image in the wild. To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans. In contrast to previous competitions or challenges, the aim of this new benchmark dataset is to evaluate the accuracy of a 3D dense face reconstruction algorithm using real, accurate and high-resolution 3D ground truth face scans. In addition to the dataset, we provide a standard protocol as well as a Python script for the evaluation. Last, we report the results obtained by three state-of-the-art 3D face reconstruction systems on the new benchmark dataset. The competition is organised along with the 2018 13th IEEE Conference on Automatic Face & Gesture Recognition.