# WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose

arXiv: https://arxiv.org/abs/2005.10353 · HTML: https://mlsanity.com/paper/2005.10353/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2020.

## Built on by (most cited first, 30 of 47)

- [3D-aware Image Synthesis via Learning Structural and Textural Representations](https://mlsanity.com/paper/2112.10759/) (2021)
- [PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360circ](https://mlsanity.com/paper/2303.13071/) (2023)
- [6D Rotation Representation For Unconstrained Head Pose Estimation](https://mlsanity.com/paper/2202.12555/) (2022)
- [FaceXFormer: A Unified Transformer for Facial Analysis](https://mlsanity.com/paper/2403.12960/) (2024)
- [DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single Image](https://mlsanity.com/paper/2204.03688/) (2022)
- [SphereHead: Stable 3D Full-head Synthesis with Spherical Tri-plane Representation](https://mlsanity.com/paper/2404.05680/) (2024)
- [MODA: Mapping-Once Audio-driven Portrait Animation with Dual Attentions](https://mlsanity.com/paper/2307.10008/) (2023)
- [MyStyle: A Personalized Generative Prior](https://mlsanity.com/paper/2203.17272/) (2022)
- [Towards Robust and Unconstrained Full Range of Rotation Head Pose Estimation](https://mlsanity.com/paper/2309.07654/) (2023)
- [LARGE: Latent-Based Regression through GAN Semantics](https://mlsanity.com/paper/2107.11186/) (2021)
- [Learning 3D-aware Image Synthesis with Unknown Pose Distribution](https://mlsanity.com/paper/2301.07702/) (2023)
- [Face-MLLM: A Large Face Perception Model](https://mlsanity.com/paper/2410.20717/) (2024)
- [On the representation and methodology for wide and short range head pose estimation](https://mlsanity.com/paper/2401.05807/) (2024)
- [A multimodal dataset for understanding the impact of mobile phones on remote online virtual education](https://mlsanity.com/paper/2412.14195/) (2024)
- [Biometrics and Behavior Analysis for Detecting Distractions in e-Learning](https://mlsanity.com/paper/2405.15434/) (2024)
- [HHP-Net: A light Heteroscedastic neural network for Head Pose estimation with uncertainty](https://mlsanity.com/paper/2111.01440/) (2021)
- [Disguise without Disruption: Utility-Preserving Face De-Identification](https://mlsanity.com/paper/2303.13269/) (2023)
- [DirectMHP: Direct 2D Multi-Person Head Pose Estimation with Full-range Angles](https://mlsanity.com/paper/2302.01110/) (2023)
- [DSFNet: Dual Space Fusion Network for Occlusion-Robust 3D Dense Face Alignment](https://mlsanity.com/paper/2305.11522/) (2023)
- [Interaction-aware Joint Attention Estimation Using People Attributes](https://mlsanity.com/paper/2308.05382/) (2023)
- [Multi-modal Affect Analysis using standardized data within subjects in the Wild](https://mlsanity.com/paper/2107.03009/) (2021)
- [LwPosr: Lightweight Efficient Fine-Grained Head Pose Estimation](https://mlsanity.com/paper/2202.03544/) (2022)
- [VGGHeads: 3D Multi Head Alignment with a Large-Scale Synthetic Dataset](https://mlsanity.com/paper/2407.18245/) (2024)
- [2D Image head pose estimation via latent space regression under occlusion settings](https://mlsanity.com/paper/2311.06038/) (2023)
- [Mathematical Foundation and Corrections for Full Range Head Pose Estimation](https://mlsanity.com/paper/2403.18104/) (2024)
- [MOS: A Low Latency and Lightweight Framework for Face Detection, Landmark Localization, and Head Pose Estimation](https://mlsanity.com/paper/2110.10953/) (2021)
- [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)
- [A Multimodal Dataset of Student Oral Presentations with Sensors and Evaluation Data](https://mlsanity.com/paper/2601.07576/) (2026)
- [UET-Headpose: A sensor-based top-view head pose dataset](https://mlsanity.com/paper/2111.07039/) (2021)

## Abstract (arXiv)

We present an end-to-end head-pose estimation network designed to predict Euler angles through the full range head yaws from a single RGB image. Existing methods perform well for frontal views but few target head pose from all viewpoints. This has applications in autonomous driving and retail. Our network builds on multi-loss approaches with changes to loss functions and training strategies adapted to wide range estimation. Additionally, we extract ground truth labelings of anterior views from a current panoptic dataset for the first time. The resulting Wide Headpose Estimation Network (WHENet) is the first fine-grained modern method applicable to the full-range of head yaws (hence wide) yet also meets or beats state-of-the-art methods for frontal head pose estimation. Our network is compact and efficient for mobile devices and applications.