  Human motion generation is a critical task with a wide range of applications.
Achieving high realism in generated motions requires naturalness, smoothness,
and plausibility. Despite rapid advancements in the field, current generation
methods often fall short of these goals. Furthermore, existing evaluation
metrics typically rely on ground-truth-based errors, simple heuristics, or
distribution distances, which do not align well with human perceptions of
motion quality. In this work, we propose a data-driven approach to bridge this
gap by introducing a large-scale human perceptual evaluation dataset,
MotionPercept, and a human motion critic model, MotionCritic, that capture
human perceptual preferences. Our critic model offers a more accurate metric
for assessing motion quality and could be readily integrated into the motion
generation pipeline to enhance generation quality. Extensive experiments
demonstrate the effectiveness of our approach in both evaluating and improving
the quality of generated human motions by aligning with human perceptions. Code
and data are publicly available at https://motioncritic.github.io/.
