# Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition

arXiv: https://arxiv.org/abs/1803.11366 · HTML: https://mlsanity.com/paper/1803.11366/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2018.

## Built on by (most cited first, 27 of 27)

- [3D face reconstruction with dense landmarks](https://mlsanity.com/paper/2204.02776/) (2022)
- [Learning to Resize Images for Computer Vision Tasks](https://mlsanity.com/paper/2103.09950/) (2021)
- [FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction](https://mlsanity.com/paper/2111.01082/) (2021)
- [Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification](https://mlsanity.com/paper/2308.10658/) (2023)
- [REALY: Rethinking the Evaluation of 3D Face Reconstruction](https://mlsanity.com/paper/2203.09729/) (2022)
- [SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction](https://mlsanity.com/paper/2106.03021/) (2021)
- [FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude](https://mlsanity.com/paper/2306.17206/) (2023)
- [Physical-World Optical Adversarial Attacks on 3D Face Recognition](https://mlsanity.com/paper/2205.13412/) (2022)
- [Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait](https://mlsanity.com/paper/2505.04616/) (2025)
- [PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition](https://mlsanity.com/paper/2107.11721/) (2021)
- [3D Face Reconstruction: the Road to Forensics](https://mlsanity.com/paper/2309.11357/) (2023)
- [RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs](https://mlsanity.com/paper/2302.05486/) (2023)
- [Weakly-Supervised Multi-Face 3D Reconstruction](https://mlsanity.com/paper/2101.02000/) (2021)
- [Recent Advances in Deep Learning Techniques for Face Recognition](https://mlsanity.com/paper/2103.10492/) (2021)
- [3D Face Reconstruction for Forensic Recognition -- A Survey](https://mlsanity.com/paper/2303.11164/) (2023)
- [MULLER: Multilayer Laplacian Resizer for Vision](https://mlsanity.com/paper/2304.02859/) (2023)
- [AVFace: Towards Detailed Audio-Visual 4D Face Reconstruction](https://mlsanity.com/paper/2304.13115/) (2023)
- [INFAMOUS-NeRF: ImproviNg FAce MOdeling Using Semantically-Aligned Hypernetworks with Neural Radiance Fields](https://mlsanity.com/paper/2312.16197/) (2023)
- [Reconstructing Recognizable 3D Face Shapes based on 3D Morphable Models](https://mlsanity.com/paper/2104.03515/) (2021)
- [Unpaired Multi-domain Attribute Translation of 3D Facial Shapes with a Square and Symmetric Geometric Map](https://mlsanity.com/paper/2308.13245/) (2023)
- [A Review of 3D Face Reconstruction From a Single Image](https://mlsanity.com/paper/2110.09299/) (2021)
- [Sphere Face Model:A 3D Morphable Model with Hypersphere Manifold Latent Space](https://mlsanity.com/paper/2112.02238/) (2021)
- [BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs](https://mlsanity.com/paper/2203.05908/) (2022)
- [A Survey of Face Recognition](https://mlsanity.com/paper/2212.13038/) (2022)
- [3D Face Reconstruction Error Decomposed: A Modular Benchmark for Fair and Fast Method Evaluation](https://mlsanity.com/paper/2505.18025/) (2025)
- [Fake It Without Making It: Conditioned Face Generation for Accurate 3D Face Reconstruction](https://mlsanity.com/paper/2307.13639/) (2023)
- [Hierarchical MLANet: Multi-level Attention for 3D Face Reconstruction From Single Images](https://mlsanity.com/paper/2509.10024/) (2025)

## Abstract (arXiv)

This paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D face shapes and learning discriminative shape features for face recognition can be accomplished simultaneously. Unlike existing 3D face reconstruction methods, our proposed method directly regresses dense 3D face shapes from single 2D images, and tackles identity and residual (i.e., non-identity) components in 3D face shapes explicitly and separately based on a composite 3D face shape model with latent representations. We devise a training process for the proposed network with a joint loss measuring both face identification error and 3D face shape reconstruction error. To construct training data we develop a method for fitting 3D morphable model (3DMM) to multiple 2D images of a subject. Comprehensive experiments have been done on MICC, BU3DFE, LFW and YTF databases. The results show that our method expands the capacity of 3DMM for capturing discriminative shape features and facial detail, and thus outperforms existing methods both in 3D face reconstruction accuracy and in face recognition accuracy.