Beyond Rigid: Benchmarking Non-Rigid Video Editing
Bingzheng Qu, Kehai Chen, Xuefeng Bai, Jun Yu, Min Zhang

TL;DR
This paper introduces NRVBench, a comprehensive benchmark for evaluating non-rigid video editing, including a new dataset, evaluation metric, and a training-free baseline, to improve physical plausibility and temporal consistency.
Contribution
The paper presents the first dedicated benchmark for non-rigid video editing, including a dataset, a novel evaluation metric, and a structure-aware editing baseline.
Findings
Current methods struggle with physical plausibility.
The proposed VM-Edit baseline outperforms existing approaches.
NRVBench provides a standard platform for physics-aware video editing evaluation.
Abstract
Despite the remarkable progress in text-driven video editing, generating coherent non-rigid deformations remains a critical challenge, often plagued by physical distortion and temporal flicker. To bridge this gap, we propose NRVBench, the first dedicated and comprehensive benchmark designed to evaluate non-rigid video editing. First, we curate a high-quality dataset consisting of 180 non-rigid motion videos from six physics-based categories, equipped with 2,340 fine-grained task instructions and 360 multiple-choice questions. Second, we propose NRVE-Acc, a novel evaluation metric based on Vision-Language Models that can rigorously assess physical compliance, temporal consistency, and instruction alignment, overcoming the limitations of general metrics in capturing complex dynamics. Third, we introduce a training-free baseline, VM-Edit, which utilizes a dual-region denoising mechanism to…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Video Analysis and Summarization · 3D Shape Modeling and Analysis
