Biomechanical plausibility of generative AI models: A validation methodology for studying cultural motor accents.

Abstract

The rapid advancement of AI video generation models presents new opportunities to reduce financial and logistical barriers in biomechanical research in diverse cultural contexts. However, the capacity of these tools to generate movement with physical fidelity remains insufficiently validated. This study evaluated the performance of Google VEO 3.1 Fast in representing a culturally defined motor task: the Japanese seiza sitting posture. Kinematic measures of actual human performance were compared with AI-generated sequences using OpenPose for pose estimation purposes. AI sequences were generated from reference frames extracted from the original performance, including virtual camera rotations produced using Qwen AI Image Edit to enable novel view synthesis. All kinematic trajectories were temporally normalized before analysis. The results indicated that while the model achieved high temporal correspondence for knee flexion (r = 0.936), it introduced substantial discrepancies in postural control. In particular, AI-generated movements exhibited a tendency toward postural regularization, reducing trunk flexion toward a more idealized vertical orientation, with mean absolute errors of approximately 10° relative to the original performance. Furthermore, subtle motor variations, such as eccentric control phases and micro-pauses, were systematically smoothed. These findings indicate that current generative video models are not yet capable of faithful biomechanical reconstruction, as they homogenize motor execution and compromise the preservation of cultural motor accents essential for rigorous quantitative analysis.

Keywords: Biomechanics, Generative AI, Cultural Movement, Motor Accents, Pose Estimation

DOI: 10.54941/ahfe1007324