SAM4Dcap: Training-free Biomechanical Twin System from Monocular Video

TL;DR

SAM4Dcap addresses the barrier of accessible biomechanical analysis from monocular video by delivering a training-free pipeline that converts temporally consistent 4D meshes (via SAM-Body4D) into OpenSim-compatible TRC trajectories, enabling inverse kinematics and dynamics with OpenCap or AddBiomechanics backends. The method integrates automated prompting with box-and-point prompts, HMR-to-TRC conversion, and flexible backends, without requiring additional training data. Preliminary results on walking and drop-jump show knee kinematics approaching multi-view systems, with some hip flexion discrepancies and residual jitter, indicating potential but incomplete parity with laboratory setups. This work bridges cutting-edge computer vision with established biomechanics, offering an open-source, Linux-friendly framework for non-laboratory motion analysis and a path toward broader home and clinical deployment.

Abstract

Quantitative biomechanical analysis is essential for clinical diagnosis and injury prevention but is often restricted to laboratories due to the high cost of optical motion capture systems. While multi-view video approaches have lowered barriers, they remain impractical for home-based scenarios requiring monocular capture. This paper presents SAM4Dcap, an open-source, end-to-end framework for estimating biomechanical metrics from monocular video without additional training. SAM4Dcap integrates the temporally consistent 4D human mesh recovery of SAM-Body4D with the OpenSim biomechanical solver. The pipeline converts reconstructed meshes into trajectory files compatible with diverse musculoskeletal models. We introduce automated prompting strategies and a Linux-native build for processing. Preliminary evaluations on walking and drop-jump tasks indicate that SAM4Dcap has the potential to achieve knee kinematic predictions comparable to multi-view systems, although some discrepancies in hip flexion and residual jitter remain. By bridging advanced computer vision with established biomechanical simulation, SAM4Dcap provides a flexible, accessible foundation for non-laboratory motion analysis.

Figures (7)

• Figure 1: Overall workflow of SAM4Dcap
• Figure 2: Automated Prompt
• Figure 3: Workflow pseudo code
• Figure 4: Walk (Knee)
• Figure 5: Walk (Hip)