# 3FabRec: Fast Few-shot Face alignment by Reconstruction

arXiv: https://arxiv.org/abs/1911.10448 · HTML: https://mlsanity.com/paper/1911.10448/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2019.

## Built on by (most cited first, 30 of 33)

- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [General Facial Representation Learning in a Visual-Linguistic Manner](https://mlsanity.com/paper/2112.03109/) (2021)
- [Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks](https://mlsanity.com/paper/2203.17030/) (2022)
- [3D face reconstruction with dense landmarks](https://mlsanity.com/paper/2204.02776/) (2022)
- [MARLIN: Masked Autoencoder for facial video Representation LearnINg](https://mlsanity.com/paper/2211.06627/) (2022)
- [Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning](https://mlsanity.com/paper/2203.06541/) (2022)
- [STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection](https://mlsanity.com/paper/2306.02763/) (2023)
- [Pre-training strategies and datasets for facial representation learning](https://mlsanity.com/paper/2103.16554/) (2021)
- [Which images to label for few-shot medical landmark detection?](https://mlsanity.com/paper/2112.04386/) (2021)
- [HIH: Towards More Accurate Face Alignment via Heatmap in Heatmap](https://mlsanity.com/paper/2104.03100/) (2021)
- [InfAnFace: Bridging the infant-adult domain gap in facial landmark estimation in the wild](https://mlsanity.com/paper/2110.08935/) (2021)
- [UOD: Universal One-shot Detection of Anatomical Landmarks](https://mlsanity.com/paper/2306.07615/) (2023)
- [One-Shot Medical Landmark Localization by Edge-Guided Transform and Noisy Landmark Refinement](https://mlsanity.com/paper/2208.00453/) (2022)
- [Meta-Point Learning and Refining for Category-Agnostic Pose Estimation](https://mlsanity.com/paper/2403.13647/) (2024)
- [T-FAKE: Synthesizing Thermal Images for Facial Landmarking](https://mlsanity.com/paper/2408.15127/) (2024)
- [Few-shot Geometry-Aware Keypoint Localization](https://mlsanity.com/paper/2303.17216/) (2023)
- [Few-Shot Model Adaptation for Customized Facial Landmark Detection, Segmentation, Stylization and Shadow Removal](https://mlsanity.com/paper/2104.09457/) (2021)
- [Mesh-Tension Driven Expression-Based Wrinkles for Synthetic Faces](https://mlsanity.com/paper/2210.03529/) (2022)
- [FreeEnricher: Enriching Face Landmarks without Additional Cost](https://mlsanity.com/paper/2212.09525/) (2022)
- [ProS: Facial Omni-Representation Learning via Prototype-based Self-Distillation](https://mlsanity.com/paper/2311.01929/) (2023)
- [Rethinking Self-training for Semi-supervised Landmark Detection: A Selection-free Approach](https://mlsanity.com/paper/2404.04556/) (2024)
- [An Efficient Multitask Neural Network for Face Alignment, Head Pose Estimation and Face Tracking](https://mlsanity.com/paper/2103.07615/) (2021)
- [Attention based Occlusion Removal for Hybrid Telepresence Systems](https://mlsanity.com/paper/2112.01098/) (2021)
- [Reasoning Structural Relation for Occlusion-Robust Facial Landmark Localization](https://mlsanity.com/paper/2112.10087/) (2021)
- [CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization](https://mlsanity.com/paper/2411.17845/) (2024)
- [Recurrent Feature Mining and Keypoint Mixup Padding for Category-Agnostic Pose Estimation](https://mlsanity.com/paper/2503.21140/) (2025)
- [Separable Batch Normalization for Robust Facial Landmark Localization with Cross-protocol Network Training](https://mlsanity.com/paper/2101.06663/) (2021)
- [Mitigating Knowledge Discrepancies among Multiple Datasets for Task-agnostic Unified Face Alignment](https://mlsanity.com/paper/2503.22359/) (2025)
- [Heatmap Regression without Soft-Argmax for Facial Landmark Detection](https://mlsanity.com/paper/2508.14929/) (2025)
- [LDDMM-Face: Large Deformation Diffeomorphic Metric Learning for Flexible and Consistent Face Alignment](https://mlsanity.com/paper/2108.00690/) (2021)

## Abstract (arXiv)

Current supervised methods for facial landmark detection require a large amount of training data and may suffer from overfitting to specific datasets due to the massive number of parameters. We introduce a semi-supervised method in which the crucial idea is to first generate implicit face knowledge from the large amounts of unlabeled images of faces available today. In a first, completely unsupervised stage, we train an adversarial autoencoder to reconstruct faces via a low-dimensional face embedding. In a second, supervised stage, we interleave the decoder with transfer layers to retask the generation of color images to the prediction of landmark heatmaps. Our framework (3FabRec) achieves state-of-the-art performance on several common benchmarks and, most importantly, is able to maintain impressive accuracy on extremely small training sets down to as few as 10 images. As the interleaved layers only add a low amount of parameters to the decoder, inference runs at several hundred FPS on a GPU.