VEFX-Bench: A Holistic Benchmark for Generic Video Editing and Visual Effects

2026-04-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Computer Vision · Depth: Expert, quick

Summary

VEFX-Bench introduces a new benchmark and dataset for instruction-guided video editing and visual effects, addressing the current lack of comprehensive evaluation resources. The VEFX-Dataset comprises 5,049 human-annotated video editing examples spanning 9 major and 32 subcategories, with quality labels across Instruction Following, Rendering Quality, and Edit Exclusivity. Complementing this, VEFX-Reward is a specialized reward model for video editing quality assessment, which processes source video, instructions, and edited video to predict per-dimension scores using ordinal regression. The VEFX-Bench benchmark itself consists of 300 curated video-prompt pairs for standardized system comparison. Experiments demonstrate VEFX-Reward's superior alignment with human judgments compared to generic vision-language models and existing reward models. Benchmarking commercial and open-source systems with VEFX-Reward highlights ongoing challenges in visual plausibility, instruction following, and edit locality.

Key takeaway

For research scientists developing or evaluating AI-assisted video editing systems, you should integrate VEFX-Bench and VEFX-Reward into your workflow. This will enable more standardized and human-aligned assessment of model performance, particularly concerning instruction following, rendering quality, and edit exclusivity, helping to identify and address current model limitations in these areas.

Key insights

VEFX-Bench provides a holistic benchmark, dataset, and reward model for evaluating instruction-guided video editing systems.

Principles

• Human annotation is crucial for nuanced quality assessment.
• Decoupled quality dimensions improve evaluation granularity.
• Specialized reward models outperform generic VLM judges.

Method

VEFX-Reward assesses video editing quality by jointly processing source video, editing instruction, and edited video, predicting per-dimension quality scores via ordinal regression.

In practice

• Use VEFX-Dataset for training video editing models.
• Integrate VEFX-Reward for automated quality assessment.
• Benchmark systems using VEFX-Bench's 300 pairs.

Topics

• VEFX-Bench
• VEFX-Dataset
• VEFX-Reward
• Video Editing
• Instruction-Guided Video Creation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.