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

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, extended

Summary

VEFX-Bench, released on April 17, 2026, introduces a comprehensive benchmark and evaluation framework for instruction-guided video editing and visual effects. The core components include VEFX-Dataset, a human-annotated dataset of 5,049 video editing examples across 9 major categories and 32 subcategories, each scored along three decoupled dimensions: Instruction Following (IF), Rendering Quality (RQ), and Edit Exclusivity (EE). Building on this, VEFX-Reward is a specialized reward model trained via ordinal regression to assess video editing quality by jointly processing source video, editing instructions, and edited video. The VEFX-Bench itself comprises 300 curated video-prompt pairs for standardized system comparison. Experiments demonstrate that VEFX-Reward aligns more strongly with human judgments than generic vision-language models and prior reward models, achieving up to 0.780 SRCC and 0.790 PLCC. Benchmarking commercial and open-source systems revealed a persistent gap in instruction following and edit locality, highlighting the need for multi-dimensional evaluation.

Key takeaway

For research scientists and computer vision engineers developing or evaluating video editing systems, VEFX-Bench provides essential resources to overcome current evaluation limitations. You should integrate VEFX-Reward into your development pipeline for automated, human-aligned quality assessment across instruction following, rendering quality, and edit exclusivity. This multi-dimensional approach will help you identify and address specific failure modes, particularly in instruction faithfulness and content preservation, which are critical for advancing AI-assisted video creation beyond basic visual plausibility.

Key insights

Multi-dimensional human-annotated datasets and specialized reward models are crucial for robust video editing evaluation.

Principles

• Video editing quality requires decoupled evaluation dimensions.
• Ordinal regression is effective for discrete human preference scales.
• Task-specific reward models outperform generic VLM judges.

Method

VEFX-Reward jointly processes source video, editing instruction, and edited video, predicting per-dimension quality scores (IF, RQ, EE) via ordinal regression using Qwen3-VL backbones at 4B and 32B scales.

In practice

• Use VEFX-Reward for automated video editing quality assessment.
• Benchmark video editing systems with VEFX-Bench's 300 curated pairs.
• Prioritize instruction following and edit exclusivity in model development.

Topics

• VEFX-Bench
• VEFX-Dataset
• VEFX-Reward
• Instruction Following
• Rendering Quality

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.