# BiHand: Recovering Hand Mesh with Multi-stage Bisected Hourglass Networks

arXiv: https://arxiv.org/abs/2008.05079 · HTML: https://mlsanity.com/paper/2008.05079/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 23 of 23)

- [ArtiBoost: Boosting Articulated 3D Hand-Object Pose Estimation via Online Exploration and Synthesis](https://mlsanity.com/paper/2109.05488/) (2021)
- [MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Image](https://mlsanity.com/paper/2112.02753/) (2021)
- [Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration](https://mlsanity.com/paper/2103.02845/) (2021)
- [A Skeleton-Driven Neural Occupancy Representation for Articulated Hands](https://mlsanity.com/paper/2109.11399/) (2021)
- [Towards Accurate Alignment in Real-time 3D Hand-Mesh Reconstruction](https://mlsanity.com/paper/2109.01723/) (2021)
- [3D Interacting Hand Pose Estimation by Hand De-occlusion and Removal](https://mlsanity.com/paper/2207.11061/) (2022)
- [Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements](https://mlsanity.com/paper/2111.00763/) (2021)
- [DART: Articulated Hand Model with Diverse Accessories and Rich Textures](https://mlsanity.com/paper/2210.07650/) (2022)
- [High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition](https://mlsanity.com/paper/2307.05541/) (2023)
- [H2O: A Benchmark for Visual Human-human Object Handover Analysis](https://mlsanity.com/paper/2104.11466/) (2021)
- [HandTailor: Towards High-Precision Monocular 3D Hand Recovery](https://mlsanity.com/paper/2102.09244/) (2021)
- [Domain Adaptive Hand Keypoint and Pixel Localization in the Wild](https://mlsanity.com/paper/2203.08344/) (2022)
- [HandBooster: Boosting 3D Hand-Mesh Reconstruction by Conditional Synthesis and Sampling of Hand-Object Interactions](https://mlsanity.com/paper/2403.18575/) (2024)
- [Identity-Aware Hand Mesh Estimation and Personalization from RGB Images](https://mlsanity.com/paper/2209.10840/) (2022)
- [MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints](https://mlsanity.com/paper/2404.10227/) (2024)
- [HandOS: 3D Hand Reconstruction in One Stage](https://mlsanity.com/paper/2412.01537/) (2024)
- [UniHOPE: A Unified Approach for Hand-Only and Hand-Object Pose Estimation](https://mlsanity.com/paper/2503.13303/) (2025)
- [CLIP-Hand3D: Exploiting 3D Hand Pose Estimation via Context-Aware Prompting](https://mlsanity.com/paper/2309.16140/) (2023)
- [SiMA-Hand: Boosting 3D Hand-Mesh Reconstruction by Single-to-Multi-View Adaptation](https://mlsanity.com/paper/2402.01389/) (2024)
- [GeoHand: Unlocking Prior Geometry Knowledge for Monocular 3D Hand Reconstruction](https://mlsanity.com/paper/2605.17354/) (2026)
- [Lightweight Estimation of Hand Mesh and Biomechanically Feasible Kinematic Parameters](https://mlsanity.com/paper/2303.14838/) (2023)
- [Gesture-Aware Pretraining and Token Fusion for 3D Hand Pose Estimation](https://mlsanity.com/paper/2603.17396/) (2026)
- [VEPHand: View-Efficient Photometric Hand Performance Capture at Scale](https://mlsanity.com/paper/2606.15966/) (2026)

## Abstract (arXiv)

3D hand estimation has been a long-standing research topic in computer vision. A recent trend aims not only to estimate the 3D hand joint locations but also to recover the mesh model. However, achieving those goals from a single RGB image remains challenging. In this paper, we introduce an end-to-end learnable model, BiHand, which consists of three cascaded stages, namely 2D seeding stage, 3D lifting stage, and mesh generation stage. At the output of BiHand, the full hand mesh will be recovered using the joint rotations and shape parameters predicted from the network. Inside each stage, BiHand adopts a novel bisecting design which allows the networks to encapsulate two closely related information (e.g. 2D keypoints and silhouette in 2D seeding stage, 3D joints, and depth map in 3D lifting stage, joint rotations and shape parameters in the mesh generation stage) in a single forward pass. As the information represents different geometry or structure details, bisecting the data flow can facilitate optimization and increase robustness. For quantitative evaluation, we conduct experiments on two public benchmarks, namely the Rendered Hand Dataset (RHD) and the Stereo Hand Pose Tracking Benchmark (STB). Extensive experiments show that our model can achieve superior accuracy in comparison with state-of-the-art methods, and can produce appealing 3D hand meshes in several severe conditions.