# DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image

arXiv: https://arxiv.org/abs/2204.03688 · HTML: https://mlsanity.com/paper/2204.03688/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 90 references that are in the map.
- First posted on arXiv 7 Apr 2022.

## Builds on (most cited first, 30 of 37)

- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [Learning an Animatable Detailed 3D Face Model from In-The-Wild Images](https://mlsanity.com/paper/2012.04012/) (2020)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [3D Morphable Face Models -- Past, Present and Future](https://mlsanity.com/paper/1909.01815/) (2019)
- [Neural Voice Puppetry: Audio-driven Facial Reenactment](https://mlsanity.com/paper/1912.05566/) (2019)
- [Towards Fast, Accurate and Stable 3D Dense Face Alignment](https://mlsanity.com/paper/2009.09960/) (2020)
- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [FaceScape: a Large-scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction](https://mlsanity.com/paper/2003.13989/) (2020)
- [Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision](https://mlsanity.com/paper/1905.06817/) (2019)
- [Fine-Grained Head Pose Estimation Without Keypoints](https://mlsanity.com/paper/1710.00925/) (2017)
- [Face Alignment in Full Pose Range: A 3D Total Solution](https://mlsanity.com/paper/1804.01005/) (2018)
- [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)
- [HOPE-Net: A Graph-based Model for Hand-Object Pose Estimation](https://mlsanity.com/paper/2004.00060/) (2020)
- [HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition](https://mlsanity.com/paper/1603.01249/) (2016)
- [LipSync3D: Data-Efficient Learning of Personalized 3D Talking Faces from Video using Pose and Lighting Normalization](https://mlsanity.com/paper/2106.04185/) (2021)
- [img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation](https://mlsanity.com/paper/2012.07791/) (2020)
- [Combining 3D Morphable Models: A Large scale Face-and-Head Model](https://mlsanity.com/paper/1903.03785/) (2019)
- [WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose](https://mlsanity.com/paper/2005.10353/) (2020)
- [An All-In-One Convolutional Neural Network for Face Analysis](https://mlsanity.com/paper/1611.00851/) (2016)
- [3D Face Morphable Models "In-the-Wild"](https://mlsanity.com/paper/1701.05360/) (2017)
- [A Vector-based Representation to Enhance Head Pose Estimation](https://mlsanity.com/paper/2010.07184/) (2020)
- [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)
- [3D Face Modeling From Diverse Raw Scan Data](https://mlsanity.com/paper/1902.04943/) (2019)
- [Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources](https://mlsanity.com/paper/1703.00862/) (2017)

## Built on by (most cited first, 30 of 31)

- [Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos](https://mlsanity.com/paper/2207.11094/) (2022)
- [3D Face Reconstruction with the Geometric Guidance of Facial Part Segmentation](https://mlsanity.com/paper/2312.00311/) (2023)
- [Instant Multi-View Head Capture through Learnable Registration](https://mlsanity.com/paper/2306.07437/) (2023)
- [UniTalker: Scaling up Audio-Driven 3D Facial Animation through A Unified Model](https://mlsanity.com/paper/2408.00762/) (2024)
- [Position: Measure Dataset Diversity, Don't Just Claim It](https://mlsanity.com/paper/2407.08188/) (2024)
- [HiFace: High-Fidelity 3D Face Reconstruction by Learning Static and Dynamic Details](https://mlsanity.com/paper/2303.11225/) (2023)
- [On the representation and methodology for wide and short range head pose estimation](https://mlsanity.com/paper/2401.05807/) (2024)
- [Reinforced Disentanglement for Face Swapping without Skip Connection](https://mlsanity.com/paper/2307.07928/) (2023)
- [DEGAS: Detailed Expressions on Full-Body Gaussian Avatars](https://mlsanity.com/paper/2408.10588/) (2024)
- [DynamicFace: High-Quality and Consistent Face Swapping for Image and Video using Composable 3D Facial Priors](https://mlsanity.com/paper/2501.08553/) (2025)
- [FacialFlowNet: Advancing Facial Optical Flow Estimation with a Diverse Dataset and a Decomposed Model](https://mlsanity.com/paper/2409.05396/) (2024)
- [DirectMHP: Direct 2D Multi-Person Head Pose Estimation with Full-range Angles](https://mlsanity.com/paper/2302.01110/) (2023)
- [S2TD-Face: Reconstruct a Detailed 3D Face with Controllable Texture from a Single Sketch](https://mlsanity.com/paper/2408.01218/) (2024)
- [VGGHeads: 3D Multi Head Alignment with a Large-Scale Synthetic Dataset](https://mlsanity.com/paper/2407.18245/) (2024)
- [Disjoint Pose and Shape for 3D Face Reconstruction](https://mlsanity.com/paper/2308.13903/) (2023)
- [2D Image head pose estimation via latent space regression under occlusion settings](https://mlsanity.com/paper/2311.06038/) (2023)
- [An Intuitive and Unconstrained 2D Cube Representation for Simultaneous Head Detection and Pose Estimation](https://mlsanity.com/paper/2212.03623/) (2022)
- [Semi-Supervised Unconstrained Head Pose Estimation in the Wild](https://mlsanity.com/paper/2404.02544/) (2024)
- [FaceLift: Semi-supervised 3D Facial Landmark Localization](https://mlsanity.com/paper/2405.19646/) (2024)
- [3D Face Alignment Through Fusion of Head Pose Information and Features](https://mlsanity.com/paper/2308.13327/) (2023)
- [MUNCH: Modelling Unique 'N Controllable Heads](https://mlsanity.com/paper/2310.02753/) (2023)
- [Latent Embedding Clustering for Occlusion Robust Head Pose Estimation](https://mlsanity.com/paper/2403.20251/) (2024)
- [AUGlasses: Continuous Action Unit based Facial Reconstruction with Low-power IMUs on Smart Glasses](https://mlsanity.com/paper/2405.13289/) (2024)
- [On the power of data augmentation for head pose estimation](https://mlsanity.com/paper/2407.05357/) (2024)
- [Bidirectional Regression for Monocular 6DoF Head Pose Estimation and Reference System Alignment](https://mlsanity.com/paper/2407.14136/) (2024)
- [3D2M Dataset: A 3-Dimension diverse Mesh Dataset](https://mlsanity.com/paper/2410.07415/) (2024)
- [AI-driven Remote Facial Skin Hydration and TEWL Assessment from Selfie Images: A Systematic Solution](https://mlsanity.com/paper/2509.06282/) (2025)
- [A Controllable 3D Deepfake Generation Framework with Gaussian Splatting](https://mlsanity.com/paper/2509.11624/) (2025)
- [Profile-Specific 3DMM Regression from a Single Lateral Face Image](https://mlsanity.com/paper/2605.01746/) (2026)
- [Bengal-HP_RU: A Dataset of Bengal People For Head Pose Estimation](https://mlsanity.com/paper/2606.24122/) (2026)

## Abstract (arXiv)

We present DAD-3DHeads, a dense and diverse large-scale dataset, and a robust model for 3D Dense Head Alignment in the wild. It contains annotations of over 3.5K landmarks that accurately represent 3D head shape compared to the ground-truth scans. The data-driven model, DAD-3DNet, trained on our dataset, learns shape, expression, and pose parameters, and performs 3D reconstruction of a FLAME mesh. The model also incorporates a landmark prediction branch to take advantage of rich supervision and co-training of multiple related tasks. Experimentally, DAD-3DNet outperforms or is comparable to the state-of-the-art models in (i) 3D Head Pose Estimation on AFLW2000-3D and BIWI, (ii) 3D Face Shape Reconstruction on NoW and Feng, and (iii) 3D Dense Head Alignment and 3D Landmarks Estimation on DAD-3DHeads dataset. Finally, the diversity of DAD-3DHeads in camera angles, facial expressions, and occlusions enables a benchmark to study in-the-wild generalization and robustness to distribution shifts. The dataset webpage is https://p.farm/research/dad-3dheads.