Human motion generation has found widespread applications in AR/VR, film, sports, and medical rehabilitation, offering a cost-effective alternative to traditional motion capture systems. However, evaluating the fidelity of such generated motions is a crucial, multifaceted task. Although previous approaches have attempted at motion fidelity evaluation using human perception or physical constraints, there remains an inherent gap between human-perceived fidelity and physical feasibility. Moreover, the subjective and coarse binary labeling of human perception further undermines the development of a robust data-driven metric. We address these issues by introducing a physical labeling method. This method evaluates motion fidelity by calculating the minimum modifications needed for a motion to align with physical laws. With this approach, we are able to produce fine-grained, continuous physical alignment annotations that serve as objective ground truth. With these annotations, we propose PP-Motion, a novel data-driven metric to evaluate both physical and perceptual fidelity of human motion. To effectively capture underlying physical priors, we employ Pearson's correlation loss for the training of our metric. Additionally, by incorporating a human-based perceptual fidelity loss, our metric can capture fidelity that simultaneously considers both human perception and physical alignment. Experimental results demonstrate that our metric, PP-Motion, not only aligns with physical laws but also aligns better with human perception of motion fidelity than previous work.

翻译：人体运动生成在增强现实/虚拟现实、影视、体育和医疗康复等领域已获得广泛应用，为传统运动捕捉系统提供了一种经济高效的替代方案。然而，评估此类生成运动的保真度是一项关键且多方面的任务。尽管先前的研究尝试通过人类感知或物理约束来评估运动保真度，但人类感知的保真度与物理可行性之间始终存在固有差距。此外，人类感知的主观性和粗粒度的二元标注进一步阻碍了鲁棒的数据驱动度量标准的发展。我们通过引入一种物理标注方法来解决这些问题。该方法通过计算运动需满足物理定律所需的最小修改量来评估运动保真度。借助此方法，我们能够生成细粒度、连续的物理对齐标注，作为客观的真实基准。基于这些标注，我们提出了PP-Motion，一种新颖的数据驱动度量标准，用于同时评估人体运动的物理保真度和感知保真度。为有效捕捉底层物理先验，我们在度量标准训练中采用了皮尔逊相关损失。此外，通过结合基于人类的感知保真度损失，我们的度量标准能够捕获同时兼顾人类感知与物理对齐的保真度。实验结果表明，我们的PP-Motion度量标准不仅符合物理定律，而且相较于先前工作，能更好地与人类对运动保真度的感知保持一致。