# Dense Face Alignment

arXiv: https://arxiv.org/abs/1709.01442 · HTML: https://mlsanity.com/paper/1709.01442/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

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

- [EMOCA: Emotion Driven Monocular Face Capture and Animation](https://mlsanity.com/paper/2204.11312/) (2022)
- [SMIRK: 3D Facial Expressions through Analysis-by-Neural-Synthesis](https://mlsanity.com/paper/2404.04104/) (2024)
- [3D face reconstruction with dense landmarks](https://mlsanity.com/paper/2204.02776/) (2022)
- [SADRNet: Self-Aligned Dual Face Regression Networks for Robust 3D Dense Face Alignment and Reconstruction](https://mlsanity.com/paper/2106.03021/) (2021)
- [Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry](https://mlsanity.com/paper/2110.09772/) (2021)
- [Fast Facial Landmark Detection and Applications: A Survey](https://mlsanity.com/paper/2101.10808/) (2021)
- [Self-Supervised 3D Face Reconstruction via Conditional Estimation](https://mlsanity.com/paper/2110.04800/) (2021)
- [TEASER: Token Enhanced Spatial Modeling for Expressions Reconstruction](https://mlsanity.com/paper/2502.10982/) (2025)
- [Towards 3D Face Reconstruction in Perspective Projection: Estimating 6DoF Face Pose from Monocular Image](https://mlsanity.com/paper/2205.04126/) (2022)
- [Learning Free-Form Deformation for 3D Face Reconstruction from In-The-Wild Images](https://mlsanity.com/paper/2105.14857/) (2021)
- [DSFNet: Dual Space Fusion Network for Occlusion-Robust 3D Dense Face Alignment](https://mlsanity.com/paper/2305.11522/) (2023)
- [SPARK: Self-supervised Personalized Real-time Monocular Face Capture](https://mlsanity.com/paper/2409.07984/) (2024)
- [Weakly-Supervised Multi-Face 3D Reconstruction](https://mlsanity.com/paper/2101.02000/) (2021)
- [Beyond 3DMM: Learning to Capture High-fidelity 3D Face Shape](https://mlsanity.com/paper/2204.04379/) (2022)
- [Leveraging Deepfakes to Close the Domain Gap between Real and Synthetic Images in Facial Capture Pipelines](https://mlsanity.com/paper/2204.10746/) (2022)
- [FreeEnricher: Enriching Face Landmarks without Additional Cost](https://mlsanity.com/paper/2212.09525/) (2022)
- [3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs](https://mlsanity.com/paper/2203.04643/) (2022)
- [Knowing When to Quit: Selective Cascaded Regression with Patch Attention for Real-Time Face Alignment](https://mlsanity.com/paper/2108.00377/) (2021)
- [CSANet: Channel Spatial Attention Network for Robust 3D Face Alignment and Reconstruction](https://mlsanity.com/paper/2405.19659/) (2024)
- [Electromyography-Informed Facial Expression Reconstruction for Physiological-Based Synthesis and Analysis](https://mlsanity.com/paper/2503.09556/) (2025)
- [Accurate 3D Facial Geometry Prediction by Multi-Task, Multi-Modal, and Multi-Representation Landmark Refinement Network](https://mlsanity.com/paper/2104.08403/) (2021)
- [3D Face Alignment Through Fusion of Head Pose Information and Features](https://mlsanity.com/paper/2308.13327/) (2023)
- [Robust 3D Face Alignment with Multi-Path Neural Architecture Search](https://mlsanity.com/paper/2406.07873/) (2024)
- [FIELDS: Face reconstruction with accurate Inference of Expression using Learning with Direct Supervision](https://mlsanity.com/paper/2511.21245/) (2025)

## Abstract (arXiv)

Face alignment is a classic problem in the computer vision field. Previous works mostly focus on sparse alignment with a limited number of facial landmark points, i.e., facial landmark detection. In this paper, for the first time, we aim at providing a very dense 3D alignment for large-pose face images. To achieve this, we train a CNN to estimate the 3D face shape, which not only aligns limited facial landmarks but also fits face contours and SIFT feature points. Moreover, we also address the bottleneck of training CNN with multiple datasets, due to different landmark markups on different datasets, such as 5, 34, 68. Experimental results show our method not only provides high-quality, dense 3D face fitting but also outperforms the state-of-the-art facial landmark detection methods on the challenging datasets. Our model can run at real time during testing.