  Significant advancements have been made in video generative models recently.
Unlike image generation, video generation presents greater challenges,
requiring not only generating high-quality frames but also ensuring temporal
consistency across these frames. Despite the impressive progress, research on
metrics for evaluating the quality of generated videos, especially concerning
temporal and motion consistency, remains underexplored. To bridge this research
gap, we propose Fr\'echet Video Motion Distance (FVMD) metric, which focuses on
evaluating motion consistency in video generation. Specifically, we design
explicit motion features based on key point tracking, and then measure the
similarity between these features via the Fr\'echet distance. We conduct
sensitivity analysis by injecting noise into real videos to verify the
effectiveness of FVMD. Further, we carry out a large-scale human study,
demonstrating that our metric effectively detects temporal noise and aligns
better with human perceptions of generated video quality than existing metrics.
Additionally, our motion features can consistently improve the performance of
Video Quality Assessment (VQA) models, indicating that our approach is also
applicable to unary video quality evaluation. Code is available at
https://github.com/ljh0v0/FMD-frechet-motion-distance.
