On the Content Bias in Fréchet Video Distance
Songwei Ge, Aniruddha Mahapatra, Gaurav Parmar, Jun-Yan Zhu, Jia-Bin Huang
摘要
a) Reference Videos (b) Medium Spatial & No Temporal Corruption (c) Small Spatial & Severe Temporal Corruption FVD=317.10 FVD=310.52 Figure 1. FVD is biased towards per-frame quality than temporal consistency. FVD [72], a commonly used video generation evaluation metric, should ideally capture both spatial and temporal aspects. However, our experiments reveal a strong bias toward individual frame quality. (b) First, we apply mild spatial distortions through local warping, which results in an FVD score of 317.10. (c) Next, we induce slightly less spatial corruptions but severe temporal inconsistencies by altering each frame differently. These changes create artifacts that are noticeable to humans and evident in the spatiotemporal x-t slice, as seen in the bottom row, but surprisingly lead to a lower FVD score of 310.52. This discrepancy highlights the metric's bias towards individual frame quality. We encourage readers to view the videos with Acrobat Reader or visit our website to observe the inconsistencies.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper34
- Vivid-ZOO: Multi-View Video Generation with Diffusion ModelBing Li, Cheng Zheng, Wenxuan Zhu, Jinjie Mai 等NeurIPS 2024 · 被引用 48 次
- SF-V: Single Forward Video Generation ModelZhixing Zhang, Yanyu Li, Yushu Wu, Yanwu Xu 等NeurIPS 2024 · 被引用 43 次
- DenseDPO: Fine-Grained Temporal Preference Optimization for Video Diffusion ModelsZiyi Wu, Anil Kag, Ivan Skorokhodov, Willi Menapace 等NeurIPS 2025 · 被引用 36 次
- Fast and Memory-Efficient Video Diffusion Using Streamlined InferenceZheng Zhan, Yushu Wu, Yifan Gong, Zichong Meng 等NeurIPS 2024 · 被引用 23 次
- Self-Supervised Flow Matching for Scalable Multi-Modal SynthesisHila Chefer, Patrick Esser, Dominik Lorenz, Dustin Podell 等ICML 2026 · 被引用 13 次
它引用的顶会 Paper37
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 被引用 35,902 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Denoising Diffusion Implicit ModelsJiaming Song, Chenlin Meng, Stefano ErmonICLR 2021 · 被引用 11,743 次
- Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li 等NeurIPS 2022 · 被引用 8,965 次
- Emerging Properties in Self-Supervised Vision TransformersMathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou 等ICCV 2021 · 被引用 8,921 次
相关 Paper
- STREAM: Spatio-TempoRal Evaluation and Analysis Metric for Video Generative ModelsPum Jun Kim, Seojun Kim, Jaejun YooICLR 2024 · 被引用 11 次
- Beyond FVD: An Enhanced Evaluation Metrics for Video Generation Distribution QualityGe Ya Luo, Gian Mario Favero, Zhi Hao Luo, Alexia Jolicoeur-Martineau 等ICLR 2025
- Direct Motion Models for Assessing Generated VideosKelsey R. Allen, Carl Doersch, Guangyao Zhou, Mohammed Suhail 等ICML 2025
- FovVideoVDP: a visible difference predictor for wide field-of-view videoRafal K. Mantiuk, Gyorgy Denes, Alexandre Chapiro, Anton Kaplanyan 等SIGGRAPH 2021 · 被引用 158 次
- EvalCrafter: Benchmarking and Evaluating Large Video Generation ModelsYaofang Liu, Xiaodong Cun, Xuebo Liu, Xintao Wang 等CVPR 2024
