# Survey on 3D face reconstruction from uncalibrated images

arXiv: https://arxiv.org/abs/2011.05740 · HTML: https://mlsanity.com/paper/2011.05740/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Nov 2020.

## Built on by (most cited first, 24 of 24)

- [FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration](https://mlsanity.com/paper/2108.06428/) (2021)
- [Towards Metrical Reconstruction of Human Faces](https://mlsanity.com/paper/2204.06607/) (2022)
- [FaceDiffuser: Speech-Driven 3D Facial Animation Synthesis Using Diffusion](https://mlsanity.com/paper/2309.11306/) (2023)
- [3D face reconstruction with dense landmarks](https://mlsanity.com/paper/2204.02776/) (2022)
- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [Instant Multi-View Head Capture through Learnable Registration](https://mlsanity.com/paper/2306.07437/) (2023)
- [FaceXHuBERT: Text-less Speech-driven E(X)pressive 3D Facial Animation Synthesis Using Self-Supervised Speech Representation Learning](https://mlsanity.com/paper/2303.05416/) (2023)
- [HiFace: High-Fidelity 3D Face Reconstruction by Learning Static and Dynamic Details](https://mlsanity.com/paper/2303.11225/) (2023)
- [3D Face Tracking from 2D Video through Iterative Dense UV to Image Flow](https://mlsanity.com/paper/2404.09819/) (2024)
- [Video-driven Neural Physically-based Facial Asset for Production](https://mlsanity.com/paper/2202.05592/) (2022)
- [Implicit Shape and Appearance Priors for Few-Shot Full Head Reconstruction](https://mlsanity.com/paper/2310.08784/) (2023)
- [3D Face Reconstruction: the Road to Forensics](https://mlsanity.com/paper/2309.11357/) (2023)
- [Multi-NeuS: 3D Head Portraits from Single Image with Neural Implicit Functions](https://mlsanity.com/paper/2209.04436/) (2022)
- [A Perceptual Shape Loss for Monocular 3D Face Reconstruction](https://mlsanity.com/paper/2310.19580/) (2023)
- [Learning a Generalized Physical Face Model From Data](https://mlsanity.com/paper/2402.19477/) (2024)
- [3D Face Reconstruction for Forensic Recognition -- A Survey](https://mlsanity.com/paper/2303.11164/) (2023)
- [Exploiting Multiple Representations: 3D Face Biometrics Fusion with Application to Surveillance](https://mlsanity.com/paper/2504.18886/) (2025)
- [Exploring 3D Face Reconstruction and Fusion Methods for Face Verification: A Case-Study in Video Surveillance](https://mlsanity.com/paper/2409.10481/) (2024)
- [GeoAvatar: Adaptive Geometrical Gaussian Splatting for 3D Head Avatar](https://mlsanity.com/paper/2507.18155/) (2025)
- [BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs](https://mlsanity.com/paper/2203.05908/) (2022)
- [Shape-from-Template with Generalised Camera](https://mlsanity.com/paper/2508.13791/) (2025)
- [An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving](https://mlsanity.com/paper/2109.02629/) (2021)
- [3D2M Dataset: A 3-Dimension diverse Mesh Dataset](https://mlsanity.com/paper/2410.07415/) (2024)
- [BabyFlow: 3D modeling of realistic and expressive infant faces](https://mlsanity.com/paper/2512.19560/) (2025)

## Abstract (arXiv)

Recently, a lot of attention has been focused on the incorporation of 3D data into face analysis and its applications. Despite providing a more accurate representation of the face, 3D facial images are more complex to acquire than 2D pictures. As a consequence, great effort has been invested in developing systems that reconstruct 3D faces from an uncalibrated 2D image. However, the 3D-from-2D face reconstruction problem is ill-posed, thus prior knowledge is needed to restrict the solutions space. In this work, we review 3D face reconstruction methods proposed in the last decade, focusing on those that only use 2D pictures captured under uncontrolled conditions. We present a classification of the proposed methods based on the technique used to add prior knowledge, considering three main strategies, namely, statistical model fitting, photometry, and deep learning, and reviewing each of them separately. In addition, given the relevance of statistical 3D facial models as prior knowledge, we explain the construction procedure and provide a list of the most popular publicly available 3D facial models. After the exhaustive study of 3D-from-2D face reconstruction approaches, we observe that the deep learning strategy is rapidly growing since the last few years, becoming the standard choice in replacement of the widespread statistical model fitting. Unlike the other two strategies, photometry-based methods have decreased in number due to the need for strong underlying assumptions that limit the quality of their reconstructions compared to statistical model fitting and deep learning methods. The review also identifies current challenges and suggests avenues for future research.