MonoMSK: Monocular 3D Musculoskeletal Dynamics Estimation

• URL: http://arxiv.org/abs/2511.19326v1
• Date: Mon, 24 Nov 2025 17:20:17 GMT
• Title: MonoMSK: Monocular 3D Musculoskeletal Dynamics Estimation
• Authors: Farnoosh Koleini, Hongfei Xue, Ahmed Helmy, Pu Wang,
• Abstract summary: We introduce MonoMSK, a hybrid framework that bridges data-driven learning and physics-based simulation.<n>MonoMSK jointly recovers both kinematics (motions) and kinetics (forces and torques) through an anatomically accurate musculoskeletal model.<n> Experiments on BML-MoVi, BEDLAM, and OpenCap show that MonoMSK significantly outperforms state-of-the-art methods in kinematic accuracy.
• Score: 9.3102957142944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstructing biomechanically realistic 3D human motion - recovering both kinematics (motion) and kinetics (forces) - is a critical challenge. While marker-based systems are lab-bound and slow, popular monocular methods use oversimplified, anatomically inaccurate models (e.g., SMPL) and ignore physics, fundamentally limiting their biomechanical fidelity. In this work, we introduce MonoMSK, a hybrid framework that bridges data-driven learning and physics-based simulation for biomechanically realistic 3D human motion estimation from monocular video. MonoMSK jointly recovers both kinematics (motions) and kinetics (forces and torques) through an anatomically accurate musculoskeletal model. By integrating transformer-based inverse dynamics with differentiable forward kinematics and dynamics layers governed by ODE-based simulation, MonoMSK establishes a physics-regulated inverse-forward loop that enforces biomechanical causality and physical plausibility. A novel forward-inverse consistency loss further aligns motion reconstruction with the underlying kinetic reasoning. Experiments on BML-MoVi, BEDLAM, and OpenCap show that MonoMSK significantly outperforms state-of-the-art methods in kinematic accuracy, while for the first time enabling precise monocular kinetics estimation.

Related papers

• Half-Physics: Enabling Kinematic 3D Human Model with Physical Interactions [89.88331682333198]
  We introduce a novel approach that embeds SMPL-X into a tangible entity capable of dynamic physical interactions with its surroundings.<n>Our approach maintains kinematic control over inherent SMPL-X poses while ensuring physically plausible interactions with scenes and objects.<n>Unlike reinforcement learning-based methods, which demand extensive and complex training, our half-physics method is learning-free and generalizes to any body shape and motion.
  arXiv  Detail & Related papers  (2025-07-31T17:58:33Z)
• Biomechanics-Guided Residual Approach to Generalizable Human Motion Generation and Estimation [21.750804738752105]
  We propose BioVAE, a biomechanics-aware framework with three core innovations.<n>We show that BioVAE achieves state-of-the-art performance on multiple benchmarks.
  arXiv  Detail & Related papers  (2025-03-08T10:22:36Z)
• Optimal-state Dynamics Estimation for Physics-based Human Motion Capture from Videos [6.093379844890164]
  We propose a novel method to selectively incorporate the physics models with the kinematics observations in an online setting.<n>A recurrent neural network is introduced to realize a Kalman filter that attentively balances the kinematics input and simulated motion.<n>The proposed approach excels in the physics-based human pose estimation task and demonstrates the physical plausibility of the predictive dynamics.
  arXiv  Detail & Related papers  (2024-10-10T10:24:59Z)
• MS-MANO: Enabling Hand Pose Tracking with Biomechanical Constraints [50.61346764110482]
  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.
  arXiv  Detail & Related papers  (2024-04-16T02:18:18Z)
• 3D Kinematics Estimation from Video with a Biomechanical Model and Synthetic Training Data [4.130944152992895]
  We propose a novel biomechanics-aware network that directly outputs 3D kinematics from two input views. Our experiments demonstrate that the proposed approach, only trained on synthetic data, outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-02-20T17:33:40Z)
• Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening [59.88594294676711]
  Modern deep learning based motion synthesis approaches barely consider the physical plausibility of synthesized motions. We propose a system Skeleton2Humanoid'' which performs physics-oriented motion correction at test time. Experiments on the challenging LaFAN1 dataset show our system can outperform prior methods significantly in terms of both physical plausibility and accuracy.
  arXiv  Detail & Related papers  (2022-10-09T16:15:34Z)
• D&D: Learning Human Dynamics from Dynamic Camera [55.60512353465175]
  We present D&D (Learning Human Dynamics from Dynamic Camera), which leverages the laws of physics to reconstruct 3D human motion from the in-the-wild videos with a moving camera. Our approach is entirely neural-based and runs without offline optimization or simulation in physics engines.
  arXiv  Detail & Related papers  (2022-09-19T06:51:02Z)
• Trajectory Optimization for Physics-Based Reconstruction of 3d Human Pose from Monocular Video [31.96672354594643]
  We focus on the task of estimating a physically plausible articulated human motion from monocular video. Existing approaches that do not consider physics often produce temporally inconsistent output with motion artifacts. We show that our approach achieves competitive results with respect to existing physics-based methods on the Human3.6M benchmark.
  arXiv  Detail & Related papers  (2022-05-24T18:02:49Z)
• PhysCap: Physically Plausible Monocular 3D Motion Capture in Real Time [89.68248627276955]
  Marker-less 3D motion capture from a single colour camera has seen significant progress. However, it is a very challenging and severely ill-posed problem. We present PhysCap, the first algorithm for physically plausible, real-time and marker-less human 3D motion capture.
  arXiv  Detail & Related papers  (2020-08-20T10:46:32Z)
• Contact and Human Dynamics from Monocular Video [73.47466545178396]
  Existing deep models predict 2D and 3D kinematic poses from video that are approximately accurate, but contain visible errors. We present a physics-based method for inferring 3D human motion from video sequences that takes initial 2D and 3D pose estimates as input.
  arXiv  Detail & Related papers  (2020-07-22T21:09:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.