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/.

翻译：人体运动生成是一项具有广泛应用的关键任务。实现生成运动的高真实感需要自然性、流畅性与合理性。尽管该领域发展迅速，当前生成方法仍常未能达到这些目标。此外，现有评估指标通常依赖于基于真实数据的误差、简单启发式规则或分布距离，这些指标与人类对运动质量的感知并不一致。本研究提出一种数据驱动方法以弥合这一差距：通过引入大规模人类感知评估数据集MotionPercept，以及能够捕捉人类感知偏好的人体运动评判模型MotionCritic。我们的评判模型提供了更精确的运动质量评估指标，并可无缝集成到运动生成流程中以提升生成质量。大量实验证明，通过实现与人类感知的对齐，我们的方法在评估与提升生成人体运动质量方面均具有显著效果。代码与数据已公开于https://motioncritic.github.io/。