MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints

• URL: http://arxiv.org/abs/2404.10227v1
• Date: Tue, 16 Apr 2024 02:18:18 GMT
• Title: MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints
• Authors: Pengfei Xie, Wenqiang Xu, Tutian Tang, Zhenjun Yu, Cewu Lu,
• Abstract summary: We integrate a musculoskeletal system with a learnable parametric hand model, MANO, to create MS-MANO. This model emulates the dynamics of muscles and tendons to drive the skeletal system, imposing physiologically realistic constraints on the resulting torque trajectories. We also propose a simulation-in-the-loop pose refinement framework, BioPR, that refines the initial estimated pose through a multi-layer perceptron network.
• Score: 50.61346764110482
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This work proposes a novel learning framework for visual hand dynamics analysis that takes into account the physiological aspects of hand motion. The existing models, which are simplified joint-actuated systems, often produce unnatural motions. To address this, we integrate a musculoskeletal system with a learnable parametric hand model, MANO, to create a new model, MS-MANO. This model emulates the dynamics of muscles and tendons to drive the skeletal system, imposing physiologically realistic constraints on the resulting torque trajectories. We further propose a simulation-in-the-loop pose refinement framework, BioPR, that refines the initial estimated pose through a multi-layer perceptron (MLP) network. Our evaluation of the accuracy of MS-MANO and the efficacy of the BioPR is conducted in two separate parts. The accuracy of MS-MANO is compared with MyoSuite, while the efficacy of BioPR is benchmarked against two large-scale public datasets and two recent state-of-the-art methods. The results demonstrate that our approach consistently improves the baseline methods both quantitatively and qualitatively.

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