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

Source: arXiv - 2604.16272v1

Overview

The paper introduces VEFX‑Bench, a new end‑to‑end benchmark for instruction‑guided video editing and visual effects. By pairing a large, human‑annotated dataset (VEFX‑Dataset) with a purpose‑built reward model (VEFX‑Reward), the authors provide the first standardized way to evaluate how well AI systems follow editing instructions, preserve visual quality, and keep edits localized.

Key Contributions

• VEFX‑Dataset: 5,049 real‑world video editing examples covering 9 major editing categories (e.g., color grading, object removal, motion transfer) and 32 sub‑categories, each annotated on three orthogonal dimensions:
  1. Instruction Following – does the output satisfy the textual prompt?
  2. Rendering Quality – visual fidelity, artifacts, temporal consistency.
  3. Edit Exclusivity – are changes confined to the intended region/time?
• VEFX‑Reward: a multimodal reward model that jointly ingests the source video, the natural‑language instruction, and the edited video, and outputs per‑dimension quality scores via ordinal regression.
• VEFX‑Bench: a curated set of 300 source‑video / instruction pairs for consistent, reproducible benchmarking of any video‑editing system.
• Comprehensive Evaluation: Demonstrates that VEFX‑Reward correlates significantly better with human judgments than generic vision‑language model judges and prior reward models, across standard IQA/VQA metrics and group‑wise preference tests.
• Empirical Survey: Benchmarks a mix of commercial (e.g., Adobe Firefly, Runway) and open‑source (e.g., Stable Diffusion Video, Sora‑Lite) editors, exposing a persistent gap between visual plausibility, instruction compliance, and edit locality.

Methodology

1. Data Collection & Annotation

• Curated raw footage from royalty‑free video libraries.
• Crafted natural‑language editing instructions for each clip (e.g., “Add a sunrise glow to the sky”).
• Obtained edited outputs from a diverse set of existing video‑editing models.
• Human annotators rated each output on the three dimensions using a 5‑point ordinal scale, ensuring inter‑annotator agreement through double‑blind review.

2. Reward Model Architecture

• Backbone: A video encoder (e.g., TimeSformer) processes the source and edited clips frame‑wise, producing spatio‑temporal embeddings.
• Instruction Encoder: A transformer‑based language model (e.g., CLIP‑Text) encodes the prompt.
• Fusion: Cross‑attention layers let the model reason about how the edit relates to the instruction and the original footage.
• Output Heads: Three parallel ordinal regression heads predict scores for instruction following, rendering quality, and edit exclusivity.

3. Training & Validation

• Trained on 80 % of VEFX‑Dataset, validated on the remaining 20 % using a pairwise ranking loss to encourage correct ordering of quality levels.
• Fine‑tuned hyper‑parameters to maximize Spearman’s ρ against held‑out human scores.

4. Benchmark Construction

• Selected 300 video‑prompt pairs that span the full taxonomy and exhibit varied difficulty (e.g., subtle color tweaks vs. large‑scale object insertion).
• Released the pairs with reference annotations but without the edited outputs, enabling fair “blind” evaluation of any system.

Results & Findings

• Higher correlation: VEFX‑Reward consistently outperforms generic vision‑language judges, confirming that a task‑specific reward model captures nuances (e.g., temporal flicker, unintended background changes) that generic models miss.
• Model gap: Even the best commercial system scores ~0.65 on instruction following but only ~0.48 on edit exclusivity, indicating that current pipelines often “over‑edit” or leave residual artifacts.
• Open‑source lag: Open‑source models trail commercial offerings by ~15 % on all dimensions, highlighting opportunities for community‑driven improvements.

Practical Implications

• Standardized Evaluation Pipeline: Developers can plug VEFX‑Reward into their training loops as a differentiable loss or as a post‑hoc evaluator, accelerating rapid iteration without costly human studies.
• Fine‑Tuning Guidance: The three decoupled scores pinpoint failure modes (e.g., good visual quality but poor instruction adherence), enabling targeted fine‑tuning or data augmentation.
• Product Benchmarking: Companies building AI video editors now have a public, reproducible benchmark (VEFX‑Bench) to compare against competitors and to showcase progress to customers.
• Safety & Trust: By explicitly measuring edit exclusivity, the benchmark discourages “hallucination” of unintended content—a key concern for brand‑safe video generation.
• Research Roadmap: The dataset’s taxonomy can serve as a curriculum for multi‑task learning, encouraging models that handle a broader spectrum of edits (e.g., lighting, motion, compositing) within a single architecture.

Limitations & Future Work

• Domain Coverage: The source videos are primarily short, royalty‑free clips; longer, high‑resolution productions (e.g., cinematic footage) are under‑represented.
• Subjectivity in Scoring: While the three dimensions reduce ambiguity, some edits (artistic style changes) still involve subjective judgments that may vary across cultures.
• Reward Model Generalization: VEFX‑Reward is trained on the specific distribution of VEFX‑Dataset; its performance on out‑of‑distribution prompts (e.g., 3D animation frames) remains to be validated.
• Real‑Time Constraints: The current reward model is computationally heavy (full video encoding). Future work could explore lightweight approximations for on‑device evaluation.
• Extension to Multimodal Prompts: Incorporating reference images or audio cues alongside text instructions is a natural next step to broaden the benchmark’s applicability.

Bottom line: VEFX‑Bench equips developers with a robust, human‑aligned yardstick for AI‑driven video editing, turning a previously ad‑hoc evaluation landscape into a repeatable, data‑driven process. By adopting the benchmark and reward model, teams can accelerate model improvement, reduce reliance on expensive human reviews, and ultimately deliver more reliable, controllable video editing tools to end‑users.

Authors

• Xiangbo Gao
• Sicong Jiang
• Bangya Liu
• Xinghao Chen
• Minglai Yang
• Siyuan Yang
• Mingyang Wu
• Jiongze Yu
• Qi Zheng
• Haozhi Wang
• Jiayi Zhang
• Jared Yang
• Jie Yang
• Zihan Wang
• Qing Yin
• Zhengzhong Tu

Paper Information

• arXiv ID: 2604.16272v1
• Categories: cs.CV, cs.AI, cs.CL
• Published: April 17, 2026
• PDF: Download PDF