VM-BHINet:Vision Mamba Bimanual Hand Interaction Network for 3D Interacting Hand Mesh Recovery From a Single RGB Image

• URL: http://arxiv.org/abs/2504.14618v1
• Date: Sun, 20 Apr 2025 13:54:22 GMT
• Title: VM-BHINet:Vision Mamba Bimanual Hand Interaction Network for 3D Interacting Hand Mesh Recovery From a Single RGB Image
• Authors: Han Bi, Ge Yu, Yu He, Wenzhuo Liu, Zijie Zheng,
• Abstract summary: Vision Mamba Bimanual Hand Interaction Network (VM-BHINet) introduces state space models (SSMs) into hand reconstruction to enhance interaction modeling.<n>The core component, Vision Mamba Interaction Feature Extraction Block (VM-IFEBlock), combines SSMs with local and global feature operations.<n> Experiments on the InterHand2.6M dataset show that VM-BHINet reduces Mean per-joint position error (MPJPE) and Mean per-vertex position error (MPVPE) by 2-3%.
• Score: 13.009696075460521
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding bimanual hand interactions is essential for realistic 3D pose and shape reconstruction. However, existing methods struggle with occlusions, ambiguous appearances, and computational inefficiencies. To address these challenges, we propose Vision Mamba Bimanual Hand Interaction Network (VM-BHINet), introducing state space models (SSMs) into hand reconstruction to enhance interaction modeling while improving computational efficiency. The core component, Vision Mamba Interaction Feature Extraction Block (VM-IFEBlock), combines SSMs with local and global feature operations, enabling deep understanding of hand interactions. Experiments on the InterHand2.6M dataset show that VM-BHINet reduces Mean per-joint position error (MPJPE) and Mean per-vertex position error (MPVPE) by 2-3%, significantly surpassing state-of-the-art methods.

Related papers

• Aligning Foundation Model Priors and Diffusion-Based Hand Interactions for Occlusion-Resistant Two-Hand Reconstruction [50.952228546326516]
  Two-hand reconstruction from monocular images faces persistent challenges due to complex and dynamic hand postures and occlusions. Existing approaches struggle with such alignment issues, often resulting in misalignment and penetration artifacts. We propose a novel framework that attempts to precisely align hand poses and interactions by integrating foundation model-driven 2D priors with diffusion-based interaction refinement.
  arXiv  Detail & Related papers  (2025-03-22T14:42:27Z)
• WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild [53.288327629960364]
  We present a data-driven pipeline for efficient multi-hand reconstruction in the wild.<n>The proposed pipeline is composed of two components: a real-time fully convolutional hand localization and a high-fidelity transformer-based 3D hand reconstruction model.<n>Our approach outperforms previous methods in both efficiency and accuracy on popular 2D and 3D benchmarks.
  arXiv  Detail & Related papers  (2024-09-18T18:46:51Z)
• DICE: End-to-end Deformation Capture of Hand-Face Interactions from a Single Image [98.29284902879652]
  We present DICE, the first end-to-end method for Deformation-aware hand-face Interaction reCovEry from a single image.<n>It features disentangling the regression of local deformation fields and global mesh locations into two network branches.<n>It achieves state-of-the-art performance on a standard benchmark and in-the-wild data in terms of accuracy and physical plausibility.
  arXiv  Detail & Related papers  (2024-06-26T00:08:29Z)
• 3D Hand Mesh Recovery from Monocular RGB in Camera Space [3.0453197258042213]
  This study proposes a network model that performs parallel processing of root-relative grids and root recovery tasks. We utilize an implicit learning approach for 2D heatmaps, enhancing the compatibility of 2D cues across different subtasks. Our proposed model is comparable with state-of-the-art models.
  arXiv  Detail & Related papers  (2024-05-12T05:36:37Z)
• Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects [89.95728475983263]
  holistic 3Dunderstanding of such interactions from egocentric views is important for tasks in robotics, AR/VR, action recognition and motion generation. We design the HANDS23 challenge based on the AssemblyHands and ARCTIC datasets with carefully designed training and testing splits. Based on the results of the top submitted methods and more recent baselines on the leaderboards, we perform a thorough analysis on 3D hand(-object) reconstruction tasks.
  arXiv  Detail & Related papers  (2024-03-25T05:12:21Z)
• 3D Hand Reconstruction via Aggregating Intra and Inter Graphs Guided by Prior Knowledge for Hand-Object Interaction Scenario [8.364378460776832]
  We propose a 3D hand reconstruction network combining the benefits of model-based and model-free approaches to balance accuracy and physical plausibility for hand-object interaction scenario. Firstly, we present a novel MANO pose parameters regression module from 2D joints directly, which avoids the process of highly nonlinear mapping from abstract image feature.
  arXiv  Detail & Related papers  (2024-03-04T05:11:26Z)
• Decoupled Iterative Refinement Framework for Interacting Hands Reconstruction from a Single RGB Image [30.24438569170251]
  We propose a decoupled iterative refinement framework to achieve pixel-alignment hand reconstruction. Our method outperforms all existing two-hand reconstruction methods by a large margin on the InterHand2.6M dataset.
  arXiv  Detail & Related papers  (2023-02-05T15:46:57Z)
• LWA-HAND: Lightweight Attention Hand for Interacting Hand Reconstruction [2.2481284426718533]
  We propose a method called lightweight attention hand (LWA-HAND) to reconstruct hands in low flops from a single RGB image. The resulting model achieves comparable performance on the InterHand2.6M benchmark in comparison with the state-of-the-art models.
  arXiv  Detail & Related papers  (2022-08-21T06:25:56Z)
• Monocular 3D Reconstruction of Interacting Hands via Collision-Aware Factorized Refinements [96.40125818594952]
  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.
  arXiv  Detail & Related papers  (2021-11-01T08:24:10Z)
• Joint Hand-object 3D Reconstruction from a Single Image with Cross-branch Feature Fusion [78.98074380040838]
  We propose to consider hand and object jointly in feature space and explore the reciprocity of the two branches. We employ an auxiliary depth estimation module to augment the input RGB image with the estimated depth map. Our approach significantly outperforms existing approaches in terms of the reconstruction accuracy of objects.
  arXiv  Detail & Related papers  (2020-06-28T09:50:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.