arXiv AI
Jun 9
CoVEBench: Can Video Editing Models Handle Complex Instructions?
arXiv:2606. 08415v1 Announce Type: cross Abstract: While recent text-guided video editing models excel at elementary tasks (e.
OmniEdit-Bench: A Comprehensive Benchmark for Instruction-based Video Editing
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.
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arXiv Computer Vision
Aug 26
CoinVE-200K: A Large-Scale High-Quality Dataset for Compositional Instruction-Guided Video Editing
arXiv:2608.17566v2 Announce Type: replace Abstract: The quality and diversity of instruction-based video editing datasets are steadily improving, yet existing datasets mainly focus on single editing...
Hugging Face Trending Papers
Aug 18
CoinVE-200K: A Large-Scale High-Quality Dataset for Compositional Instruction-Guided Video Editing
CoinVE-200K is a large, high‑quality dataset for compositional instruction‑guided video editing, featuring 1080p video‑editing pairs up to 201 frames long and containing 2–5 atomic editing operations per sample. The dataset covers diverse editing intents—targeting humans, objects, and backgrounds with addition, removal, modification, and stylization—while ensuring instruction faithfulness, visual quality, temporal consistency, and compositional diversity through a careful generation and filtering pipeline. CoinVE-Bench benchmarks these capabilities, and CoinVE-Edit, a 22B model built on Wan2.1‑T2V‑14B and Qwen3‑VL‑8B‑Instruct, demonstrates strong performance in instruction following, compositional editing accuracy, visual quality, and temporal consistency.
arXiv AI
Sep 10
Beyond Coherence: Benchmarking Professional Editing-Technique Execution in Multi-Shot Audio-Video Generation
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...
arXiv AI
Oct 1
ViTeX-Bench: Benchmarking High-Fidelity Video Scene Text Editing
arXiv:2609.40356v1 Announce Type: cross Abstract: Recent video generation is increasingly realistic and controllable, yet video editing remains less developed, particularly for precise local edits th...
arXiv Computer Vision
Oct 9
Transforming Image Editors into Video Editors
The paper introduces Anchor-based Video Editing (AVE), a two-stage framework that converts a powerful image editor into a video editor by first editing keyframes and then propagating those edits across time using a motion-guided image-to-video diffusion model. AVE treats edited keyframes as fixed anchors, leveraging the strengths of modern image editors while avoiding costly end-to-end video training. Experiments on IVEBench and VIE-Bench demonstrate strong performance in instruction following, temporal consistency, and content fidelity, and ablations show that video quality correlates with the underlying image editor’s performance.
arXiv Computer Vision
Aug 27
RefVideo-6M: A Reliable Reference-Based Dataset for Instructional Video Editing
RefVideo-6M is a new large-scale reference-guided editing dataset that includes 5 million video editing samples and 1 million image editing samples, each paired with about 6 million visual references. The dataset is constructed to avoid artifacts by using real, artifact‑free videos as targets and filtering input conditions with multiple editing experts, thereby providing reliable supervision. It enables models to learn fine‑grained visual correspondence beyond text‑only instructions and supports the training of a reference‑guided video editing model, Ref‑MoT, which shows improved visual quality, controllability, and reference consistency.
arXiv Computer Vision
Sep 7
AVENUE: Audio-Video EditiNg Understanding and Evaluation
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.
Hugging Face Trending Papers
Jun 29
Goku: A Million-Scale Universal Dataset and Benchmark for Instruction-Based Video Editing
Existing instruction-based video editing datasets commonly focus on single-task appearance editing, failing to meet the complex creative demands of real-world scenarios. To bridge this gap, we present Goku, a large-scale dataset featuring 2 million high-quality, instruction-aligned video editing pairs, which is the first to extend task boundaries from basic appearance editing to multi-task and structural manipulations(e.
arXiv AI
Oct 9
VINCIE-NExT: Unlocking Video Editing from Images via In-Context Modeling
VINCIE‑NExT is a unified framework that transfers image editing capabilities to video editing by using in‑context visual demonstrations. It decomposes video editing into a chain of sub‑tasks (Video → Image → Image → Video), routing editing intent through the image domain and enabling joint training from heterogeneous image and video data under a single diffusion objective. The method introduces a novel position encoding to link image demonstrations and video frames, allowing pixel‑faithful appearance propagation, and supports test‑time scaling through progressive diffusion stages.
arXiv AI
Oct 21
Code2Video-Compass: A Comprehensive Benchmark for General-Purpose Code-Based Video Generation across T2V, I2V, and V2V
arXiv:2610.28416v1 Announce Type: cross Abstract: Code-based video generation offers fine-grained control, targeted editing, and component reuse compared with direct video generation. This approach r...
Hugging Face Trending Papers
Sep 28
Timeline-Bench: Evaluating Agents on Realistic Video-Editing Tasks, from Raw Footage to Final Cut
Timeline-Bench is a benchmark comprising 56 real video‑editing tasks that require AI agents to transform raw production material into finished videos. Each task includes a brief, source assets, a container, and a set of tests that assess format, content, brief compliance, and quality based on 2,582 blind judgments by 43 video editors. In evaluations, the best agent resolved only 15 of the 56 tasks, and most failures were due to quality tests rather than technical errors.