← ArchivePaper2019
Hand Modeling and Simulation Using Stabilized Magnetic Resonance Imaging
Acquires complete hand bone anatomy in multiple poses via stabilized MRI and builds animation-ready volumetric hand rigs from medical imaging data.
Abstract
We demonstrate how to acquire complete human hand bone anatomy (meshes) in multiple poses using magnetic resonance imaging (MRI). Such acquisition was previously difficult because MRI scans must be long for high-precision results (over 10 minutes) and because humans cannot hold the hand perfectly still in non-trivial and badly supported poses. We invent a manufacturing process whereby we use lifecasting materials commonly employed in film special effects industry to generate hand molds, personalized to the subject, and to each pose. These molds are both ergonomic and encasing, and they stabilize the hand during scanning. We also demonstrate how to efficiently segment the MRI scans into individual bone meshes in all poses, and how to correspond each bone's mesh to same mesh connectivity across all poses. Next, we interpolate and extrapolate the MRI-acquired bone meshes to the entire range of motion of the hand, producing an accurate data-driven animation-ready rig for bone meshes. We also demonstrate how to acquire not just bone geometry (using MRI) in each pose, but also a matching highly accurate surface geometry (using optical scanners) in each pose, modeling skin pores and wrinkles.
How to read this
- Category
- Capture system + method (MRI-based hand anatomy acquisition and rigging)
- Contributions
- A stabilization process using film-industry lifecasting materials to make personalized, encasing molds that hold the hand still across multiple poses during long MRI scans
- A workflow to segment MRI scans into individual bone meshes with consistent connectivity across all poses
- Interpolation and extrapolation of acquired bone meshes across the full range of motion to produce an animation-ready bone rig, plus matched optical surface scans capturing pores and wrinkles
- Context
- Acquires the anatomical bone data underlying biomechanical hand models, relating to simulation/control work on hands and tendinous systems (Sachdeva et al.).Builds on: Biomechanical Simulation and Control of Hands and Tendinous Systems
- Correctness
- A data-driven acquisition approach; accuracy hinges on the molds genuinely stabilizing the hand and on faithful cross-pose correspondence, and results are demonstrated per-subject, so generality and the labor of mold-making and segmentation are practical limits to note.
- Clarity
- Accessible in its motivation and pipeline; a first pass conveys the method, with segmentation and interpolation details for a second pass.
- How to read it
- First pass for the molding-and-acquisition idea and what data it yields; second pass on segmentation, correspondence and range-of-motion interpolation if you plan to build or use such a rig.
Related work
- Data-Driven Physics for Human Soft Tissue Animation 2017 / SIGGRAPH
- Steklov-Poincare Skinning 2014 / SCA
- Simulation of Hand Anatomy Using Medical Imaging 2022 / SIGGRAPH Asia
- Computational Bodybuilding: Anatomically-Based Modeling of Human Bodies 2015 / SIGGRAPH
Keywords
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