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New FAH models offer faster, more accurate 3D hand estimation

Researchers have developed FAH, a new family of hand estimation models that improve accuracy and speed by directly predicting 2D and 3D joints and surface points. These models are fine-tuned from SAM 3D Body's hand branch and can fit to MANO or MHR-Hand models, optionally estimating anatomical parts and contact. Experiments demonstrate significant improvements in alignment, geometry, contact estimation, and world-space tracking compared to existing methods. The default ViT-L model achieves 65 fps on an RTX 4070 Ti SUPER, with a smaller ViT-S model reaching 125 fps. AI

IMPACT Improves accuracy and speed for 3D hand tracking, potentially benefiting AR/VR and robotics applications.

RANK_REASON New research paper detailing a novel model for hand estimation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New FAH models offer faster, more accurate 3D hand estimation

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New research paper detailing a novel model for hand estimation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jinkun Cao ·

    Fast and Accurate Hand Estimation in Pixels, Interaction and World Space

    arXiv:2611.07711v1 Announce Type: new Abstract: Monocular hand mesh estimators commonly predict rig parameters and project the mesh to obtain 2D keypoints. We find that directly predicted 3D joints agree better with 2D predictions, and fitting a mesh to predicted points reduces m…