Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality
<|reference_start|>Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality: The Fr\'echet Video Distance (FVD) is a widely adopted metric for evaluating video generation distribution quality. However, its effectiveness relies on critical assumptions. Our analysis reveals three significant limitations: (1) the non-Gaussianity of the Inflated 3D Convnet (I3D) feature space; (2) the insensitivity of I3D features to temporal distortions; (3) the impractical sample sizes required for reliable estimation. These findings undermine FVD's reliability and show that FVD falls short as a standalone metric for video generation evaluation. After extensive analysis of a wide range of metrics and backbone architectures, we propose JEDi, the JEPA Embedding Distance, based on features derived from a Joint Embedding Predictive Architecture, measured using Maximum Mean Discrepancy with polynomial kernel. Our experiments on multiple open-source datasets show clear evidence that it is a superior alternative to the widely used FVD metric, requiring only 16% of the samples to reach its steady value, while increasing alignment with human evaluation by 34%, on average.<|reference_end|>
@article{luo2024beyond,
    title={Beyond FVD: Enhanced Evaluation Metrics for Video Generation Quality},
    author={Ge Ya Luo, Gian Mario Favero, Zhi Hao Luo, Alexia Jolicoeur-Martineau,
  Christopher Pal},
    journal={arXiv preprint arXiv:2410.05203},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.05203},
    primaryClass={cs.CV cs.AI cs.LG}
}