mlsanityData to 14 Oct 2026

Overcoming the Trade-off Between Accuracy and Plausibility in 3D Hand Shape Reconstruction

arXiv 2305.00646 ↗First posted 1 May 2023cs.CVcited by 4

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Abstract (arXiv)

Direct mesh fitting for 3D hand shape reconstruction is highly accurate. However, the reconstructed meshes are prone to artifacts and do not appear as plausible hand shapes. Conversely, parametric models like MANO ensure plausible hand shapes but are not as accurate as the non-parametric methods. In this work, we introduce a novel weakly-supervised hand shape estimation framework that integrates non-parametric mesh fitting with MANO model in an end-to-end fashion. Our joint model overcomes the tradeoff in accuracy and plausibility to yield well-aligned and high-quality 3D meshes, especially in challenging two-hand and hand-object interaction scenarios.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 39 of its 46 references that are in the map. First posted on arXiv 1 May 2023.

Builds on

39 of 46 references.

References · 39

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27 more
  1. 20193D Hand Shape and Pose from Images in the Wildcited by 190
  2. 2022Embodied Hands: Modeling and Capturing Hands and Bodies Togethercited by 181
  3. 20193D Hand Shape and Pose Estimation from a Single RGB Imagecited by 174
  4. 2020Perceiving 3D Human-Object Spatial Arrangements from a Single Image in the Wildcited by 152
  5. 2020Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wildcited by 146
  6. 2020Reconstructing Hand-Object Interactions in the Wildcited by 144
  7. 2020Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstructioncited by 139
  8. 2020CPF: Learning a Contact Potential Field to Model the Hand-Object Interactioncited by 128
  9. 2019End-to-end Hand Mesh Recovery from a Monocular RGB Imagecited by 124
  10. 2021Keypoint Transformer: Solving Joint Identification in Challenging Hands and Object Interactions for Accurate 3D Pose Estimationcited by 123
  11. 2020Monocular Real-time Hand Shape and Motion Capture using Multi-modal Datacited by 106
  12. 2022HandOccNet: Occlusion-Robust 3D Hand Mesh Estimation Networkcited by 96
  13. 2019SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operatorcited by 89
  14. 2022Interacting Attention Graph for Single Image Two-Hand Reconstructioncited by 87
  15. 2021MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Imagecited by 74
  16. 2021Model-based 3D Hand Reconstruction via Self-Supervised Learningcited by 65
  17. 2022AlignSDF: Pose-Aligned Signed Distance Fields for Hand-Object Reconstructioncited by 59
  18. 2022Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolutioncited by 54
  19. 2021I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modelingcited by 52
  20. 2021From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimationcited by 48
  21. 2019Single Image 3D Hand Reconstruction with Mesh Convolutionscited by 32
  22. 2021Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinementscited by 27
  23. 2021Coarse-to-fine Animal Pose and Shape Estimationcited by 16
  24. 2022Spatial-Temporal Parallel Transformer for Arm-Hand Dynamic Estimationcited by 8
  25. 2022UV-Based 3D Hand-Object Reconstruction with Grasp Optimizationcited by 8
  26. 2021Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimationcited by 6
  27. 20223D-Augmented Contrastive Knowledge Distillation for Image-based Object Pose Estimationcited by 2

Built on by

Cited by year

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

4 papers.

  1. 2024Weakly-Supervised 3D Hand Reconstruction with Knowledge Prior and Uncertainty Guidancecited by 5
  2. 2025Diffusion-based 3D Hand Motion Recovery with Intuitive Physicscited by 5
  3. 20243D Hand Reconstruction via Aggregating Intra and Inter Graphs Guided by Prior Knowledge for Hand-Object Interaction Scenariocited by 2
  4. 2023Monocular 3D Hand Mesh Recovery via Dual Noise Estimationcited by 0