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Musculotendon Simulation for Hand Animation
Musculotendon simulation for anatomically detailed hand animation, modeling tendon routing and muscle fiber contraction for realistic grasping.
Abstract
This paper presents an automatic technique for generating the motion of tendons and muscles under the skin of a traditionally animated character. The method integrates a standard keyframe or motion capture animation pipeline with a biomechanical simulator that uses rigid bodies for bones and spline-based strands for tendons and muscles, supporting complex routing constraints such as sliding and surface constraints. An incremental controller solves a constrained quadratic optimization to compute the muscle activation levels required to track the input skeletal animation, and the resulting subcutaneous strand motion is skinned to the character surface as a post-process. The approach is demonstrated on animations of the human hand and forearm, where the model contains 54 musculotendons and 17 bones, capturing tendon deformations on the back of the hand and thumb.
How to read this
- Category
- Method: biomechanical musculotendon simulation for animation
- Contributions
- Generates tendon and muscle motion under the skin of a traditionally animated character, integrating keyframe or mocap pipelines with a biomechanical simulator
- Models bones as rigid bodies and tendons and muscles as spline-based strands with sliding and surface routing constraints
- An incremental controller solving constrained quadratic optimization to find muscle activations that track the input skeletal animation, then skins the strand motion to the surface
- Context
- Builds on anatomical muscle simulation such as Creating and Simulating Skeletal Muscle from the Visible Human Data Set (Teran et al.), but uses strand-based musculotendons driven to track an artist's animation.Builds on: Creating and Simulating Skeletal Muscle from the Visible Human Data Set
- Correctness
- Demonstrated on a hand and forearm model with a stated count of musculotendons and bones, capturing tendon deformation on the back of the hand and thumb; readers should keep in mind it targets subcutaneous strand and tendon motion as a post-process layer, and results are shown on this anatomical region rather than as a general full-body solver.
- Clarity
- Moderately technical; a first pass conveys the track-an-animation idea, a second pass clarifies the strand constraints and activation optimization.
- How to read it
- Focus on the strand routing constraints and the activation-tracking optimization, since those are the core; a second pass pays off if you want anatomically plausible tendon motion in a rig.
Built upon by
Related work
- A Neural Network Model for Efficient Musculoskeletal-Driven Skin Deformation 2024 / SIGGRAPH
- Pose-Space Subspace Dynamics 2016 / SIGGRAPH
- Enriching Facial Blendshape Rigs with Physical Simulation 2017 / SCA
- Simulation of Hand Anatomy Using Medical Imaging 2022 / SIGGRAPH Asia
Keywords
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