mlsanityData to 14 Oct 2026

PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging

arXiv 2106.10893 ↗First posted 21 Jun 2021cs.CVcited by 14

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Abstract (arXiv)

Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare. Existing parametric models account only for hand shape, pose, or texture, without modeling the anatomical attributes like bone, which is essential for realistic hand biomechanics analysis. In this paper, we present PIANO, the first parametric bone model of human hands from MRI data. Our PIANO model is biologically correct, simple to animate, and differentiable, achieving more anatomically precise modeling of the inner hand kinematic structure in a data-driven manner than the traditional hand models based on the outer surface only. Furthermore, our PIANO model can be applied in neural network layers to enable training with a fine-grained semantic loss, which opens up the new task of data-driven fine-grained hand bone anatomic and semantic understanding from MRI or even RGB images. We make our model publicly available.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 7 of its 29 references that are in the map. First posted on arXiv 21 Jun 2021.

Builds on

7 of 29 references.

References · 7

  1. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,292
  2. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  3. 2019Learning joint reconstruction of hands and manipulated objectscited by 378
  4. 2019FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Imagescited by 271
  5. 2020KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representationscited by 49
  6. 2020DeepHandMesh: A Weakly-supervised Deep Encoder-Decoder Framework for High-fidelity Hand Mesh Modelingcited by 49
  7. 2019Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Networkcited by 9

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Citing papers per year: 2022: 7; 2023: 2; 2024: 4; 2025: 1.

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