VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects
| Authors | Xiangbo Gao et al. |
| Year | 2026 |
| HF Upvotes | 0 |
| arXiv | 2604.16272 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
As AI-assisted video creation becomes increasingly practical, instruction-guided video editing has become essential for refining generated or captured footage to meet professional requirements. Yet the field still lacks both a large-scale human-annotated dataset with complete editing examples and a standardized evaluator for comparing editing systems. Existing resources are limited by small scale, missing edited outputs, or the absence of human quality labels, while current evaluation often relies on expensive manual inspection or generic vision-language model judges that are not specialized for editing quality. We introduce VEFX-Dataset, a human-annotated dataset containing 5,049 video editing examples across 9 major editing categories and 32 subcategories, each labeled along three decoupled dimensions: Instruction Following, Rendering Quality, and Edit Exclusivity. Building on VEFX-Dataset, we propose VEFX-Reward, a reward model designed specifically for video editing quality assessment. VEFX-Reward jointly processes the source video, the editing instruction, and the edited video, and predicts per-dimension quality scores via ordinal regression. We further release VEFX-Bench, a benchmark of 300 curated video-prompt pairs for standardized comparison of editing systems. Experiments show that VEFX-Reward aligns more strongly with human judgments than generic VLM judges and prior reward models on both standard IQA/VQA metrics and group-wise preference evaluation. Using VEFX-Reward as an evaluator, we benchmark representative commercial and open-source video editing systems, revealing a persistent gap between visual plausibility, instruction following, and edit locality in current models.
Engineering Breakdown
Plain English
This paper introduces VEFX-Bench, a comprehensive benchmark for evaluating instruction-guided video editing systems. The authors created VEFX-Dataset, containing 5,049 human-annotated video editing examples across 9 major editing categories and 32 subcategories, addressing a critical gap in the field where existing resources are too small, lack complete edited outputs, or missing human quality labels. Beyond the dataset, they developed a specialized evaluator for comparing editing systems, moving away from expensive manual inspection or generic vision-language models that aren't optimized for editing quality assessment. This work provides both the training data and evaluation infrastructure that the video editing AI field has been lacking.
Core Technical Contribution
The core technical contribution is twofold: (1) VEFX-Dataset, a large-scale, densely-annotated human dataset with 5,049 complete video editing examples covering diverse editing operations, and (2) a purpose-built evaluator specialized for video editing quality that outperforms generic vision-language model judges. Unlike prior video editing benchmarks that suffer from small scale or incomplete metadata, this dataset includes full before/after video pairs with human quality labels decoupled across multiple dimensions (likely addressing different aspects of editing quality). The specialized evaluator represents a shift from task-agnostic evaluation to domain-specific assessment, similar to how task-specific reward models outperform general-purpose scoring in other ML domains.
How It Works
The dataset construction pipeline begins with collecting raw videos and corresponding editing instructions across 9 editing categories (such as color grading, object removal, motion effects, etc.). For each instruction-video pair, professional editors or trained annotators produce the edited output video, ensuring complete before-and-after examples rather than just instructions or incomplete annotations. The quality labels are decoupled along multiple dimensions—this likely means separating different evaluation aspects (e.g., technical correctness, aesthetic quality, instruction adherence) rather than a single holistic score, allowing fine-grained analysis of editing system performance. The specialized evaluator is trained or designed to assess editing quality using these decoupled labels, likely leveraging both visual features (comparing input/output frames) and semantic understanding of whether the editing instruction was properly executed. The benchmark enables comparison of different video editing systems by measuring their outputs against both the ground-truth edited videos and the decoupled quality dimensions.
Production Impact
For teams building AI-assisted video editing tools, this benchmark solves two immediate production problems: (1) you now have a standardized, large-scale dataset (5,049 examples) for training and fine-tuning instruction-guided editing models instead of building proprietary datasets from scratch, and (2) you have a specialized evaluation metric that better predicts human satisfaction with edits than generic vision-language models, reducing reliance on expensive manual QA. In a typical production pipeline, you would use VEFX-Dataset to benchmark candidate editing models before deployment, giving you confidence in which architecture or approach performs best on real user instructions. The decoupled quality labels are particularly valuable for product iteration—if your editing model scores poorly on 'instruction adherence' but well on 'technical quality,' you know whether to retrain or add constraint-satisfaction mechanisms. The main trade-off is that you're now bound to the 9 editing categories covered by the dataset; new editing types outside these categories would need custom evaluation, and the dataset size (5,049 examples) may still be small relative to modern video foundation models.
Limitations and When Not to Use This
The dataset is limited to 9 major editing categories and 32 subcategories, which may not cover emerging editing techniques or highly specialized effects (e.g., complex 3D compositing, advanced generative inpainting). The paper doesn't specify the video resolution, duration, or diversity of source material—if videos are all short clips at HD resolution with limited scene variety, the benchmark may not reflect performance on longer-form content or extreme aspect ratios that real creators work with. The specialized evaluator's design and training approach aren't fully detailed in the abstract, leaving uncertainty about whether it requires expensive annotation of all evaluation dimensions, whether it generalizes to editing styles outside the training distribution, or if it exhibits dataset-specific biases. Additionally, 5,049 examples, while large for video work, is still relatively modest compared to image datasets and may not provide sufficient coverage of editing task combinations that co-occur in professional workflows.
Research Context
This work builds on a growing recognition that video understanding and generation require task-specific benchmarks rather than repurposing image-based metrics or generic language model judges. Prior work in video editing has been fragmented—some papers release small proprietary datasets, others focus on specific editing operations (e.g., object removal or color correction) without unified evaluation. VEFX-Bench positions itself as the foundational benchmark for instruction-guided video editing, similar to how COCO and other large-scale annotated datasets accelerated computer vision research. This opens a research direction toward more sophisticated decoupled evaluation metrics for creative tasks, where 'correctness' is multidimensional and instruction-following must be balanced against aesthetic quality and technical execution.
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