HumanScore: Benchmarking Human Motions in Generated Videos
This addresses the problem of systematically measuring human motion quality in generated videos for researchers and developers in video generation, though it is incremental as it builds on existing evaluation methods.
The paper introduces HumanScore, a framework for evaluating the quality of human motions in AI-generated videos, revealing gaps between perceptual plausibility and biomechanical fidelity and identifying common failure modes like temporal jitter and anatomically implausible poses.
Recent advances in model architectures, compute, and data scale have driven rapid progress in video generation, producing increasingly realistic content. Yet, no prior method systematically measures how faithfully these systems render human bodies and motion dynamics. In this paper, we present HumanScore, a systematic framework to evaluate the quality of human motions in AI-generated videos. HumanScore defines six interpretable metrics spanning kinematic plausibility, temporal stability, and biomechanical consistency, enabling fine-grained diagnosis beyond visual realism alone. Through carefully designed prompts, we elicit a diverse set of movements at varying intensities and evaluate videos generated by thirteen state-of-the-art models. Our analysis reveals consistent gaps between perceptual plausibility and motion biomechanical fidelity, identifies recurrent failure modes (e.g., temporal jitter, anatomically implausible poses, and motion drift), and produces robust model rankings from quantitative and physically meaningful criteria.