# OccFace: Unified Occlusion-Aware Facial Landmark Detection with Per-Point Visibility

arXiv: https://arxiv.org/abs/2602.10728 · HTML: https://mlsanity.com/paper/2602.10728/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 23 of 23)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [Look at Boundary: A Boundary-Aware Face Alignment Algorithm](https://mlsanity.com/paper/1805.10483/) (2018)
- [Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression](https://mlsanity.com/paper/1904.07399/) (2019)
- [GridMask Data Augmentation](https://mlsanity.com/paper/2001.04086/) (2020)
- [LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood](https://mlsanity.com/paper/2004.02980/) (2020)
- [Facial Landmark Detection: a Literature Survey](https://mlsanity.com/paper/1805.05563/) (2018)
- [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)
- [ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment](https://mlsanity.com/paper/2109.05721/) (2021)
- [A Survey of AI-Generated Video Evaluation](https://mlsanity.com/paper/2410.19884/) (2024)
- [HIH: Towards More Accurate Face Alignment via Heatmap in Heatmap](https://mlsanity.com/paper/2104.03100/) (2021)
- [Landmark Detection and 3D Face Reconstruction for Caricature using a Nonlinear Parametric Model](https://mlsanity.com/paper/2004.09190/) (2020)
- [AIGVE-Tool: AI-Generated Video Evaluation Toolkit with Multifaceted Benchmark](https://mlsanity.com/paper/2503.14064/) (2025)
- [FusionViT: Hierarchical 3D Object Detection via LiDAR-Camera Vision Transformer Fusion](https://mlsanity.com/paper/2311.03620/) (2023)
- [3DifFusionDet: Diffusion Model for 3D Object Detection with Robust LiDAR-Camera Fusion](https://mlsanity.com/paper/2311.03742/) (2023)
- [Facial Landmark Detection for Manga Images](https://mlsanity.com/paper/1811.03214/) (2018)
- [ORFormer: Occlusion-Robust Transformer for Accurate Facial Landmark Detection](https://mlsanity.com/paper/2412.13174/) (2024)
- [Are AI-Generated Driving Videos Ready for Autonomous Driving? A Diagnostic Evaluation Framework](https://mlsanity.com/paper/2512.06376/) (2025)

## Built on by (most cited first, 1 of 1)

- [Dyna3: VLM-Guided Training-Free 4D Reconstruction via Depth Foundation Models](https://mlsanity.com/paper/2610.01286/) (2026)

## Abstract (arXiv)

Accurate facial landmark detection under occlusion remains challenging, especially for human-like faces with large appearance variation and rotation-driven self-occlusion. Existing detectors typically localize landmarks while handling occlusion implicitly, without predicting per-point visibility that downstream applications can benefits. We present OccFace, an occlusion-aware framework for universal human-like faces, including humans, stylized characters, and other non-human designs. OccFace adopts a unified dense 100-point layout and a heatmap-based backbone, and adds an occlusion module that jointly predicts landmark coordinates and per-point visibility by combining local evidence with cross-landmark context. Visibility supervision mixes manual labels with landmark-aware masking that derives pseudo visibility from mask-heatmap overlap. We also create an occlusion-aware evaluation suite reporting NME on visible vs. occluded landmarks and benchmarking visibility with Occ AP, F1@0.5, and ROC-AUC, together with a dataset annotated with 100-point landmarks and per-point visibility. Experiments show improved robustness under external occlusion and large head rotations, especially on occluded regions, while preserving accuracy on visible landmarks.