Aligning Human Motion Generation with Human Perceptions

• URL: http://arxiv.org/abs/2407.02272v1
• Date: Tue, 2 Jul 2024 14:01:59 GMT
• Title: Aligning Human Motion Generation with Human Perceptions
• Authors: Haoru Wang, Wentao Zhu, Luyi Miao, Yishu Xu, Feng Gao, Qi Tian, Yizhou Wang,
• Abstract summary: We propose a data-driven approach to bridge the gap by introducing a large-scale human perceptual evaluation dataset, MotionPercept, and a human motion critic model, MotionCritic. Our critic model offers a more accurate metric for assessing motion quality and could be readily integrated into the motion generation pipeline.
• Score: 51.831338643012444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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/.

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