CoVEBench: Can Video Editing Models Handle Complex Instructions?

• URL: http://arxiv.org/abs/2606.08415v2
• Date: Wed, 10 Jun 2026 04:17:35 GMT
• Title: CoVEBench: Can Video Editing Models Handle Complex Instructions?
• Abstract summary: CoVEBench is a compositional video editing benchmark comprising 416 curated source videos, 626 multi-point editing instructions, and 9,990 fine-grained checklist items.<n>It evaluates models via MLLM-judged instruction compliance and video fidelity, alongside automated metrics for video quality.
• Score: 26.871333176496126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While recent text-guided video editing models excel at elementary tasks (e.g., style transfer, object insertion), real-world user requests are highly compositional. A single prompt often demands multiple coupled edits, such as modifying subjects, actions, and camera views, while strictly preserving unrelated spatiotemporal content. Existing benchmarks, heavily constrained by isolated edits and coarse global metrics, fail to diagnose how models handle such complex workflows. To address this gap, we introduce CoVEBench, a compositional video editing benchmark comprising 416 curated source videos, 626 multi-point editing instructions, and 9,990 fine-grained checklist items. Covering diverse editing dimensions, CoVEBench evaluates models via MLLM-judged instruction compliance and video fidelity, alongside automated metrics for video quality. Extensive experiments reveal that compositional editing remains a profound challenge: current models frequently omit edits, violate preservation constraints, or introduce artifacts when handling multiple operations simultaneously. CoVEBench provides a challenging, diagnostic testbed to advance video editing toward realistic user workflows.

Related papers

• RefVideo-6M: A Reliable Reference-Based Dataset for Instructional Video Editing [66.83679558829465]
  RefVideo-6M is a large-scale reference-guided editing dataset containing 5 million video editing samples and 1 million image editing samples.<n>To ensure reliable supervision, our dataset uses a construction pipeline that treats artifact-free real videos as editing targets.<n>It provides approximately 6 million visual references, covering diverse reference types and editing scenarios.
  arXiv  Detail & Related papers  (2026-08-26T17:59:19Z)
• OmniEdit-Bench: A Comprehensive Benchmark for Instruction-based Video Editing [53.21868575804758]
  We introduce a comprehensive and structured benchmark for instruction-based video editing (IVE)<n>Our benchmark decomposes editing tasks into multiple video-specific dimensions, including spatial, temporal, audio, and reference-based editing.<n>We propose an evaluation framework that assesses editing quality from four complementary dimensions: accuracy, preservation, realism, and consistency.
  arXiv  Detail & Related papers  (2026-08-05T16:58:48Z)
• UniEditBench: A Unified and Cost-Effective Benchmark for Image and Video Editing via Distilled MLLMs [19.634771714749544]
  We present UniEditBench, a unified benchmark for image and video editing.<n>UniEditBench supports reconstruction-based and instruction-driven methods under a shared protocol.<n>We distill a high-capacity MLLM judge into lightweight 4B/8B evaluators that provide multi-dimensional scoring over structural fidelity, text alignment, background consistency, naturalness, and temporal-spatial consistency for videos.
  arXiv  Detail & Related papers  (2026-04-17T09:21:48Z)
• EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning [58.53074381801114]
  We introduce EditVerse, a unified framework for image and video generation and editing within a single model.<n>By representing all modalities, i.e. text, image, and video, as a unified token sequence, EditVerse leverages self-attention to achieve robust in-context learning.<n>We present EditVerseBench, the first benchmark for instruction-based video editing covering diverse tasks and resolutions.
  arXiv  Detail & Related papers  (2025-09-24T17:59:30Z)
• MultiEdit: Advancing Instruction-based Image Editing on Diverse and Challenging Tasks [46.87912659985628]
  MultiEdit is a comprehensive dataset featuring over 107K high-quality image editing samples.<n>It encompasses 6 challenging editing tasks through a diverse collection of 18 non-style-transfer editing types and 38 style transfer operations.<n>We employ a novel dataset construction pipeline that utilizes two multi-modal large language models (MLLMs) to generate visual-adaptive editing instructions.
  arXiv  Detail & Related papers  (2025-09-18T05:33:38Z)
• O-DisCo-Edit: Object Distortion Control for Unified Realistic Video Editing [88.93410369258203]
  O-DisCo-Edit is a unified framework that incorporates a novel object distortion control (O-DisCo)<n>This signal, based on random and adaptive noise, flexibly encapsulates a wide range of editing cues within a single representation.<n>O-DisCo-Edit enables efficient, high-fidelity editing through an effective training paradigm.
  arXiv  Detail & Related papers  (2025-09-01T16:29:39Z)
• UNIC: Unified In-Context Video Editing [76.76077875564526]
  UNified In-Context Video Editing (UNIC) is a framework that unifies diverse video editing tasks within a single model in an in-context manner.<n>We introduce task-aware RoPE to facilitate consistent temporal positional encoding, and condition bias that enables the model to clearly differentiate different editing tasks.<n>Results demonstrate that our unified approach achieves superior performance on each task and exhibits emergent task composition abilities.
  arXiv  Detail & Related papers  (2025-06-04T17:57:43Z)
• CompBench: Benchmarking Complex Instruction-guided Image Editing [63.347846732450364]
  CompBench is a large-scale benchmark for complex instruction-guided image editing.<n>We propose an MLLM-human collaborative framework with tailored task pipelines.<n>We propose an instruction decoupling strategy that disentangles editing intents into four key dimensions.
  arXiv  Detail & Related papers  (2025-05-18T02:30:52Z)
• PRIMEdit: Probability Redistribution for Instance-aware Multi-object Video Editing with Benchmark Dataset [27.706882926164724]
  PRIMEdit is a zero-shot framework that introduces two key modules: Instance-centric Probability Redistribution and Disentangled Multi-instance Sampling.<n>We present our new MIVE dataset for video editing featuring diverse video scenarios, and introduce the Cross-Instance Accuracy (CIA) Score to evaluate editing leakage.<n>Our extensive qualitative, quantitative, and user study evaluations demonstrate that PRIMEdit significantly outperforms recent state-of-the-art methods in terms of editing faithfulness, accuracy, and leakage prevention.
  arXiv  Detail & Related papers  (2024-12-17T13:00:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.