超越刚性：非刚性视频编辑基准测试
Beyond Rigid: Benchmarking Non-Rigid Video Editing
AI总结：
提出NRVBench诊断基准，通过物理感知评估框架揭示传统指标在非刚性视频编辑中的不足，并引入VM-Edit基线分析稳定性-可塑性权衡。
摘要：
随着视频生成模型越来越需要处理物理动态，评估必须超越外观保真度和语义对齐。非刚性视频编辑提供了一个独特的揭示性测试平台，其中不同材料施加不同的物理约束。在本文中，我们引入了NRVBench，一个用于非刚性视频编辑的诊断基准，其任务是修改可变形运动，同时保留无关区域并保持材料特定的合理性。NRVBench包含180个精心策划的视频，涵盖六个基于物理的类别，2,340条细粒度编辑指令，360个多项选择题和像素精确的掩码。我们进一步提出了NRVE-Acc，一种基于VLM的结构化协议，将编辑成功分解为指令遵循、材料感知变形合理性和带有运动线索的时间一致性。对代表性推理时视频编辑方法的实验揭示了传统指标与物理感知感知编辑成功之间的明显不匹配：在非刚性动态下，保留外观或实现强全局对齐的方法可能仍然失败。我们还引入了VM-Edit，一个简单的区域条件编辑基线，它释放前景同时锁定背景，暴露了稳定性-可塑性权衡。
英文摘要：
As video generation models are increasingly expected to manipulate physical dynamics, there is a growing need to move evaluation beyond appearance fidelity and semantic alignment. Non-rigid video editing offers a uniquely revealing testbed, where distinct materials impose distinct physical constraints. In this paper, we introduce NRVBench, a diagnostic benchmark for non-rigid video editing, where the task is to modify deformable motion while preserving irrelevant regions and maintaining material-specific plausibility. NRVBench contains 180 curated videos across six physics-grounded categories, 2,340 fine-grained editing instructions, 360 multiple-choice questions, and pixel-accurate masks. We further propose NRVE-Acc, a structured VLM-based protocol that decomposes editing success into instruction following, material-aware deformation plausibility, and temporal coherence with motion cues. Experiments on representative inference-time video editing methods reveal a clear mismatch between conventional metrics and physics-aware perceptual editing success: methods that preserve appearance or achieve strong global alignment may still fail under non-rigid dynamics. We additionally introduce VM-Edit, a simple region-conditioned editing baseline that frees the foreground while locking the background, exposing the stability--plasticity trade-off.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
