mlsanityData to 7 Oct 2026

MeMaHand: Exploiting Mesh-Mano Interaction for Single Image Two-Hand Reconstruction

arXiv 2303.15718 ↗First posted 28 Mar 2023cs.CV cs.AIcited by 8

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Abstract (arXiv)

Existing methods proposed for hand reconstruction tasks usually parameterize a generic 3D hand model or predict hand mesh positions directly. The parametric representations consisting of hand shapes and rotational poses are more stable, while the non-parametric methods can predict more accurate mesh positions. In this paper, we propose to reconstruct meshes and estimate MANO parameters of two hands from a single RGB image simultaneously to utilize the merits of two kinds of hand representations. To fulfill this target, we propose novel Mesh-Mano interaction blocks (MMIBs), which take mesh vertices positions and MANO parameters as two kinds of query tokens. MMIB consists of one graph residual block to aggregate local information and two transformer encoders to model long-range dependencies. The transformer encoders are equipped with different asymmetric attention masks to model the intra-hand and inter-hand attention, respectively. Moreover, we introduce the mesh alignment refinement module to further enhance the mesh-image alignment. Extensive experiments on the InterHand2.6M benchmark demonstrate promising results over the state-of-the-art hand reconstruction methods.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 27 of its 35 references that are in the map. First posted on arXiv 28 Mar 2023.

Builds on

27 of 35 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  3. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 649
  4. 2020End-to-End Human Pose and Mesh Reconstruction with Transformerscited by 417
  5. 2019Learning joint reconstruction of hands and manipulated objectscited by 377
  6. 2020I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Imagecited by 274
  7. 2021Mesh Graphormercited by 264
  8. 2021PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loopcited by 250
  9. 2020Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Posecited by 248
  10. 2020InterHand2.6M: A Dataset and Baseline for 3D Interacting Hand Pose Estimation from a Single RGB Imagecited by 244
  11. 2017Learning to Estimate 3D Hand Pose from Single RGB Imagescited by 234
  12. 20193D Hand Shape and Pose from Images in the Wildcited by 190
15 more
  1. 2022Embodied Hands: Modeling and Capturing Hands and Bodies Togethercited by 179
  2. 20193D Hand Shape and Pose Estimation from a Single RGB Imagecited by 174
  3. 2020Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstructioncited by 139
  4. 2019End-to-end Hand Mesh Recovery from a Monocular RGB Imagecited by 124
  5. 2015Capturing Hands in Action using Discriminative Salient Points and Physics Simulationcited by 115
  6. 2019Pushing the Envelope for RGB-based Dense 3D Hand Pose Estimation via Neural Renderingcited by 115
  7. 2020Monocular Real-time Hand Shape and Motion Capture using Multi-modal Datacited by 106
  8. 2018Cross-modal Deep Variational Hand Pose Estimationcited by 89
  9. 2022Interacting Attention Graph for Single Image Two-Hand Reconstructioncited by 87
  10. 2021Model-based 3D Hand Reconstruction via Self-Supervised Learningcited by 65
  11. 2021Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registrationcited by 59
  12. 2021I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modelingcited by 52
  13. 2021Towards Accurate Alignment in Real-time 3D Hand-Mesh Reconstructioncited by 44
  14. 2021Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-pixel Part Segmentationcited by 40
  15. 2021RGB2Hands: Real-Time Tracking of 3D Hand Interactions from Monocular RGB Videocited by 23

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 2; 2026: 1.

8 papers.

  1. 2023Reconstructing Hands in 3D with Transformerscited by 298
  2. 2024Reconstructing Hand-Held Objects in 3D from Images and Videoscited by 23
  3. 2024HandOS: 3D Hand Reconstruction in One Stagecited by 9
  4. 2024Weakly-Supervised 3D Hand Reconstruction with Knowledge Prior and Uncertainty Guidancecited by 5
  5. 2025Diffusion-based 3D Hand Motion Recovery with Intuitive Physicscited by 5
  6. 2026PAD-Hand: Physics-Aware Diffusion for Hand Motion Recoverycited by 4
  7. 2023Pyramid Deep Fusion Network for Two-Hand Reconstruction from RGB-D Imagescited by 2
  8. 2025VM-BHINet:Vision Mamba Bimanual Hand Interaction Network for 3D Interacting Hand Mesh Recovery From a Single RGB Imagecited by 2