Kavli Affiliate: Wei Gao
| First 5 Authors: Shangkun Sun, Xiaoyu Liang, Songlin Fan, Wenxu Gao, Wei Gao
| Summary:
Text-driven video editing has recently experienced rapid development. Despite
this, evaluating edited videos remains a considerable challenge. Current
metrics tend to fail to align with human perceptions, and effective
quantitative metrics for video editing are still notably absent. To address
this, we introduce VE-Bench, a benchmark suite tailored to the assessment of
text-driven video editing. This suite includes VE-Bench DB, a video quality
assessment (VQA) database for video editing. VE-Bench DB encompasses a diverse
set of source videos featuring various motions and subjects, along with
multiple distinct editing prompts, editing results from 8 different models, and
the corresponding Mean Opinion Scores (MOS) from 24 human annotators. Based on
VE-Bench DB, we further propose VE-Bench QA, a quantitative human-aligned
measurement for the text-driven video editing task. In addition to the
aesthetic, distortion, and other visual quality indicators that traditional VQA
methods emphasize, VE-Bench QA focuses on the text-video alignment and the
relevance modeling between source and edited videos. It proposes a new
assessment network for video editing that attains superior performance in
alignment with human preferences. To the best of our knowledge, VE-Bench
introduces the first quality assessment dataset for video editing and an
effective subjective-aligned quantitative metric for this domain. All data and
code will be publicly available at https://github.com/littlespray/VE-Bench.
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