AVENUE is a new benchmark and evaluation framework for audio‑video editing that includes 1,291 source clips and 7,957 editing instructions covering audio‑targeted, video‑targeted, and coupled edits. It introduces a sample‑specific, modality‑aware evaluation that specifies the intended change and the content that must remain unchanged. The study applies this framework to joint, sequential, and separate editing models, revealing that existing models often alter unintended modalities, highlighting a key challenge in controllable AV editing.
arXiv:2609.08275v1 Announce Type: new Abstract: Recent multi-shot audio-video generators can produce increasingly coherent and cinematic outputs, but coherence does not imply the ability to execute e...
OmniEdit-Bench introduces a comprehensive benchmark for instruction-based video editing (IVE), addressing limitations of existing datasets by covering spatial, temporal, audio, and reference-based editing tasks and distinguishing explicit from implicit instructions. The evaluation framework assesses editing quality across accuracy, preservation, realism, and consistency, using human judgments and vision-language models, and incorporates an accuracy-aware penalty to ensure instruction fidelity. Experiments reveal that current IVE models perform poorly, highlighting the need for improved methods.
AV-FlowEdit is a training‑free, inversion‑free framework that enables prompt‑driven audio‑visual editing using joint audio‑video diffusion models. It leverages source‑to‑target flow differences to guide edits while preserving non‑target structure and cross‑modal correspondence. The method includes a Cross‑Modal Event‑Aware Spectral Trajectory Preservation module that suppresses drift by combining flow saliency, aligned audio activity, multiscale low‑frequency displacement, and diffusion‑stage scheduling, achieving strong prompt alignment, temporal consistency, and competitive audio quality.
arXiv:2607. 09973v1 Announce Type: cross Abstract: Industrial sound design requires audio generation systems that not only produce realistic audio, but also preserve the perceptual identity of a reference, support controllable variation, and remain efficient for practical workflows.
arXiv:2610.27947v1 Announce Type: new Abstract: Human-centric audio-visual editing requires precise alignment of lip movements, gestures, and expressions with speech. Existing methods process inputs...
arXiv:2606. 08415v1 Announce Type: cross Abstract: While recent text-guided video editing models excel at elementary tasks (e.
arXiv:2608. 02673v1 Announce Type: cross Abstract: Speech editing for content creation requires precise control over both what an edit should do and where it should apply.
arXiv:2606. 20101v3 Announce Type: replace-cross Abstract: Audio editing aims to modify specific content in an existing audio clip according to a text instruction or description while preserving the remaining acoustic content.
arXiv:2610.35816v1 Announce Type: cross Abstract: Editing multi-camera concerts requires coordinated decisions about musical relevance, visual quality and cut timing. We present a modular framework t...
arXiv:2610.33377v1 Announce Type: cross Abstract: Advertisers routinely create multiple duration variants of ads to meet marketing budgets and viewer preferences, a labor-intensive and costly process...
The study examines how the quality of AI-generated audio description drafts influences the editing process by novice describers. Using the GenAD pipeline and RefineAD interface, researchers found that higher-quality drafts significantly reduced completion time and cognitive load, while low-quality drafts offered only modest benefits. The results suggest a minimum quality threshold that depends on visual complexity, proposing a design principle that AI assistance should meet a content‑specific quality standard to be effective.
The paper introduces EditVid, a training‑free framework for video editing that unifies instruction‑guided and reference‑guided edits. It employs sparse causal memory for local coherence, correspondence‑based post‑attention token injection for long‑range identity preservation, and soft latent blending for edit locality. EditVid supports a wide range of editing tasks—including style transfer, attribute modification, object insertion, part‑level editing, and subject replacement—and outperforms the strongest training‑free baseline on FiVE while achieving competitive results on IVEBench, with a user study showing a 51.8% overall preference over seven competing methods.