Technical Summary: VEBENCH

Problem Statement

Real-world video editing is a complex cognitive process that demands not only expert knowledge of cinematic techniques but also advanced multimodal reasoning to select, align, and combine footage into coherent narratives. While recent Large Multimodal Models (LMMs) have demonstrated significant progress in general video understanding, their capabilities in multi-video reasoning and operational editing workflows remain largely unexplored.

Existing benchmarks in this domain (e.g., VEU-Bench, ShotBench) suffer from two primary limitations:

1. Limited Scope: They primarily evaluate knowledge-based comprehension on very short clips (averaging ~6 seconds), relying almost exclusively on visual cues while neglecting audiovisual synchronization and long-range temporal context.
2. Lack of Operational Reasoning: They overlook the core editorial workflow of selecting, aligning, and combining footage from multiple sources to construct a narrative. This requires intent-aware decision-making and cross-video reasoning, which current models lack.

Methodology

To bridge these gaps, the authors introduce VEBENCH, the first comprehensive benchmark designed to evaluate both editing knowledge understanding and operational reasoning in realistic scenarios. The benchmark is constructed through a rigorous, three-round human-AI collaborative annotation pipeline involving over 1,400 working hours.

Dataset Composition

VEBENCH comprises 3.9K high-quality edited videos (over 257 hours) and 3,080 human-verified QA pairs. The dataset covers a broad spectrum of cinematic and vlog-style content, with an average video duration of 213 seconds and 3.27 annotated editing cuts per clip.

Task Definitions

The benchmark defines two complementary QA tasks that approximate human editing cognition:

1. Video Editing Technique Recognition (TechRec):

   • Goal: Assess the model's ability to identify seven specific editing techniques using multimodal cues (visual and auditory).
   • Techniques: L-Cut, J-Cut, Jump Cut, Smash Cut, Cutaway, Invisible Cut, and Match Cut.
   • Challenge: Requires fine-grained perception of temporal transitions, cross-scene audio continuity, and rhythmic pacing.

2. Video Editing Operation Simulation (OpSim):

   • Goal: Emulate real-world editing workflows by requiring the model to select the most suitable footage from multiple candidates and localize the precise temporal segment to insert into a reference video.
   • Format: Given a reference clip (A-Roll) and a list of candidate videos, the model must choose the correct video (B-Roll) and determine the start/end timestamps for insertion (either at the beginning or end of the reference).
   • Challenge: Introduces multi-video reasoning, requiring the model to infer editing intentions and construct narrative continuity across independent sources.

Annotation Pipeline

• TechRec: Annotators locate transition points and label them with specific techniques. Verification includes cross-validation and automated checks for audio-visual alignment.
• OpSim: A multi-stage process involving LMM-assisted metadata generation, A/B-roll pairing (categorized into eight narrative relationship types like Cause-and-Effect or Illustration), candidate video selection, and distractor generation.

Key Contributions

1. Unified Framework: The formulation of a new video editing task that unifies knowledge understanding (recognition) and operational reasoning (selection/localization), providing a practical testbed for complex multimodal and multi-video reasoning.
2. VEBENCH Benchmark: A professionally annotated dataset featuring diverse, fine-grained editing scenarios collected from real-world videos, validated through a three-round human-AI collaborative process.
3. Comprehensive Evaluation: Extensive evaluations across leading proprietary (e.g., Gemini-2.5-Pro, GPT-4o) and open-source LMMs (e.g., Qwen-VL, InternVL), establishing a baseline for future research.

Experimental Results

The authors evaluated state-of-the-art models on VEBENCH, revealing a significant performance gap between current AI capabilities and human-level editing reasoning:

• Overall Performance: Even the strongest proprietary model, Gemini-2.5-Pro, struggles significantly. While it outperforms others, its scores remain far below the requirements for practical editing applications.
• TechRec: Audio cues are proven essential. Models without audio understanding perform poorly, particularly on J-Cuts and L-Cuts which rely on cross-scene sound continuity.
• OpSim:
  • Footage Selection (OpSim-FS): Models show partial reasoning ability, with Gemini-2.5-Pro achieving ~44% accuracy.
  • Temporal Localization: Nearly all models fail completely at precise temporal grounding, achieving tIoU scores close to zero. Only Gemini-2.5-Pro produced non-trivial results (tIoU ≈ 0.11), yet this remains insufficient for professional workflows.
• Ablation Studies:
  • Subtitles: The presence of subtitles consistently improves performance, acting as a lightweight cross-modal bridge for semantic grounding.
  • Visual Prompts: Continuous visual cues (on-screen text) are superior to abrupt separators (frame separators) for maintaining temporal coherence in stitched video inputs.

Limitations Identified

The evaluation highlights three critical limitations in current LMMs:

1. Weak Audiovisual Integration: Models often treat vision and audio independently, failing to capture synchronized cues necessary for techniques like J-/L-Cuts.
2. Limited Temporal Capacity: Many models are restricted to short sequences, missing fine-grained motion and stylistic changes across hundreds of frames.
3. Inaccurate Temporal Localization: Models tend to predict timestamps near the beginning of videos, indicating a lack of robust internal representation for video continuity and editing logic.

Significance

VEBENCH serves as a diagnostic tool that underscores the urgent need to bridge video understanding with creative operational reasoning. The paper claims that current models are far from achieving human-level editing intelligence. By systematically evaluating the progression from passive video comprehension to active, human-like editing, VEBENCH lays the groundwork for developing next-generation multimodal systems capable of complex reasoning in real-world video editing contexts. The authors envision this benchmark as a foundation for advancing intelligent video editing systems and driving future research in complex multimodal reasoning.