# Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements

arXiv: https://arxiv.org/abs/2111.00763 · HTML: https://mlsanity.com/paper/2111.00763/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 52 references that are in the map.
- First posted on arXiv 1 Nov 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [End-to-End Human Pose and Mesh Reconstruction with Transformers](https://mlsanity.com/paper/2012.09760/) (2020)
- [HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2011.14672/) (2020)
- [Resolving 3D Human Pose Ambiguities with 3D Scene Constraints](https://mlsanity.com/paper/1908.06963/) (2019)
- [Convolutional Mesh Regression for Single-Image Human Shape Reconstruction](https://mlsanity.com/paper/1905.03244/) (2019)
- [Integral Human Pose Regression](https://mlsanity.com/paper/1711.08229/) (2017)
- [Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose](https://mlsanity.com/paper/2008.09047/) (2020)
- [InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/2008.09309/) (2020)
- [Learning to Estimate 3D Hand Pose from Single RGB Images](https://mlsanity.com/paper/1705.01389/) (2017)
- [3D Hand Shape and Pose from Images in the Wild](https://mlsanity.com/paper/1902.03451/) (2019)
- [3D Hand Shape and Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/1903.00812/) (2019)
- [FrankMocap: A Monocular 3D Whole-Body Pose Estimation System via Regression and Integration](https://mlsanity.com/paper/2108.06428/) (2021)
- [Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wild](https://mlsanity.com/paper/2007.15649/) (2020)
- [GANerated Hands for Real-time 3D Hand Tracking from Monocular RGB](https://mlsanity.com/paper/1712.01057/) (2017)
- [Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild](https://mlsanity.com/paper/2004.01946/) (2020)
- [Coherent Reconstruction of Multiple Humans from a Single Image](https://mlsanity.com/paper/2006.08586/) (2020)
- [End-to-end Hand Mesh Recovery from a Monocular RGB Image](https://mlsanity.com/paper/1902.09305/) (2019)
- [Capturing Hands in Action using Discriminative Salient Points and Physics Simulation](https://mlsanity.com/paper/1506.02178/) (2015)
- [Pushing the Envelope for RGB-based Dense 3D Hand Pose Estimation via Neural Rendering](https://mlsanity.com/paper/1904.04196/) (2019)
- [V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map](https://mlsanity.com/paper/1711.07399/) (2017)
- [Weakly Supervised 3D Hand Pose Estimation via Biomechanical Constraints](https://mlsanity.com/paper/2003.09282/) (2020)
- [Scene-aware Generative Network for Human Motion Synthesis](https://mlsanity.com/paper/2105.14804/) (2021)
- [Real-time Pose and Shape Reconstruction of Two Interacting Hands With a Single Depth Camera](https://mlsanity.com/paper/2106.08059/) (2021)
- [Towards Social Artificial Intelligence: Nonverbal Social Signal Prediction in A Triadic Interaction](https://mlsanity.com/paper/1906.04158/) (2019)
- [Disentangling Latent Hands for Image Synthesis and Pose Estimation](https://mlsanity.com/paper/1812.01002/) (2018)

## Built on by (most cited first, 27 of 27)

- [TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinement](https://mlsanity.com/paper/2306.08637/) (2023)
- [PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images](https://mlsanity.com/paper/2207.06400/) (2022)
- [Interacting Attention Graph for Single Image Two-Hand Reconstruction](https://mlsanity.com/paper/2203.09364/) (2022)
- [HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-body Mesh Recovery](https://mlsanity.com/paper/2304.05690/) (2023)
- [TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer](https://mlsanity.com/paper/2211.10705/) (2022)
- [ACR: Attention Collaboration-based Regressor for Arbitrary Two-Hand Reconstruction](https://mlsanity.com/paper/2303.05938/) (2023)
- [3D Interacting Hand Pose Estimation by Hand De-occlusion and Removal](https://mlsanity.com/paper/2207.11061/) (2022)
- [Bringing Inputs to Shared Domains for 3D Interacting Hands Recovery in the Wild](https://mlsanity.com/paper/2303.13652/) (2023)
- [A Dataset of Relighted 3D Interacting Hands](https://mlsanity.com/paper/2310.17768/) (2023)
- [InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusion](https://mlsanity.com/paper/2403.17422/) (2024)
- [Im2Hands: Learning Attentive Implicit Representation of Interacting Two-Hand Shapes](https://mlsanity.com/paper/2302.14348/) (2023)
- [Not All Voxels Are Equal: Semantic Scene Completion from the Point-Voxel Perspective](https://mlsanity.com/paper/2112.12925/) (2021)
- [Closely Interactive Human Reconstruction with Proxemics and Physics-Guided Adaption](https://mlsanity.com/paper/2404.11291/) (2024)
- [Reconstructing Interacting Hands with Interaction Prior from Monocular Images](https://mlsanity.com/paper/2308.14082/) (2023)
- [A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation from a Single RGB Image](https://mlsanity.com/paper/2304.03635/) (2023)
- [HandDiffuse: Generative Controllers for Two-Hand Interactions via Diffusion Models](https://mlsanity.com/paper/2312.04867/) (2023)
- [Spectral Graphormer: Spectral Graph-based Transformer for Egocentric Two-Hand Reconstruction using Multi-View Color Images](https://mlsanity.com/paper/2308.11015/) (2023)
- [AttentionHand: Text-driven Controllable Hand Image Generation for 3D Hand Reconstruction in the Wild](https://mlsanity.com/paper/2407.18034/) (2024)
- [DICE: End-to-end Deformation Capture of Hand-Face Interactions from a Single Image](https://mlsanity.com/paper/2406.17988/) (2024)
- [Decoupled Iterative Refinement Framework for Interacting Hands Reconstruction from a Single RGB Image](https://mlsanity.com/paper/2302.02410/) (2023)
- [LWA-HAND: Lightweight Attention Hand for Interacting Hand Reconstruction](https://mlsanity.com/paper/2208.09815/) (2022)
- [Overcoming the Trade-off Between Accuracy and Plausibility in 3D Hand Shape Reconstruction](https://mlsanity.com/paper/2305.00646/) (2023)
- [Reconstructing Close Human Interaction with Appearance and Proxemics Reasoning](https://mlsanity.com/paper/2507.02565/) (2025)
- [PhysPose: Refining 6D Object Poses with Physical Constraints](https://mlsanity.com/paper/2503.23587/) (2025)
- [VM-BHINet:Vision Mamba Bimanual Hand Interaction Network for 3D Interacting Hand Mesh Recovery From a Single RGB Image](https://mlsanity.com/paper/2504.14618/) (2025)
- [UltraGlove: Hand Pose Estimation with Mems-Ultrasonic Sensors](https://mlsanity.com/paper/2306.12652/) (2023)
- [Two Hands Are Better Than One: Resolving Hand to Hand Intersections via Occupancy Networks](https://mlsanity.com/paper/2404.05414/) (2024)

## Abstract (arXiv)

3D interacting hand reconstruction is essential to facilitate human-machine interaction and human behaviors understanding. Previous works in this field either rely on auxiliary inputs such as depth images or they can only handle a single hand if monocular single RGB images are used. Single-hand methods tend to generate collided hand meshes, when applied to closely interacting hands, since they cannot model the interactions between two hands explicitly. In this paper, we make the first attempt to reconstruct 3D interacting hands from monocular single RGB images. Our method can generate 3D hand meshes with both precise 3D poses and minimal collisions. This is made possible via a two-stage framework. Specifically, the first stage adopts a convolutional neural network to generate coarse predictions that tolerate collisions but encourage pose-accurate hand meshes. The second stage progressively ameliorates the collisions through a series of factorized refinements while retaining the preciseness of 3D poses. We carefully investigate potential implementations for the factorized refinement, considering the trade-off between efficiency and accuracy. Extensive quantitative and qualitative results on large-scale datasets such as InterHand2.6M demonstrate the effectiveness of the proposed approach.