AVENUE: Audio-Video EditiNg Understanding and Evaluation
Audio-video (AV) editing aims to modify audio and video content according to a target prompt. Unlike single-modality editing, AV editing requires models to infer a modality-selective edit scope from the prompt alone: determining not only what should change, but also which modality should be preserved. Faithfully evaluating such models therefore requires both (i) benchmarks that span diverse edit types and modality categories, and (ii) evaluation that is itself modality-aware and sample-specific. However, existing AV editing benchmarks provide limited coverage of edit types and modality combinations, while current evaluation systems are often modality-blind and sample-agnostic, making it d...
| Authors | Hayeon Kim, Yoojin Jang, Jaejun Yoo |
|---|---|
| Primary category | cs.MM |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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