文章背景与核心概要
随着视频生成技术的飞速发展，现有的评估指标大多局限于逐帧的一致性或中间步骤的准确性，往往忽视了视频是否真正完成了指定的复杂语义任务。为了填补这一空白，本文推出了 SemComp-Bench，这是一个全新的、以结果为导向的视频生成模型评估框架。
该研究的核心在于引入了语义任务完成度（Semantic Task Completion）的概念，并通过构建包含六个不同领域的精心策划数据集（SemComp-Data），结合视觉语言模型（VLM）提出了两大关键评估指标：结果达成度（OA Score）和生成可靠性（GR Score）。初步实验表明，尽管当前视频生成模型取得了长足进步，但在精准实现任务结果和保持强大的语义基础方面，依然面临着巨大的挑战。
SemComp-Bench: Benchmarking Semantic Task Completion in Video Generation
arXiv: 2608.17426
Date: August 18, 2026
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv: 2608.17426
Date: August 18, 2026
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Summary
SemComp-Bench introduces a novel, outcome-oriented framework for evaluating video generation models. Unlike traditional benchmarks that prioritize frame-by-frame consistency or intermediate step accuracy, this approach focuses on Semantic Task Completion. The core objective is to determine whether a generated video successfully achieves a specified outcome while maintaining high-level semantic grounding relative to a reference image.
SemComp-Bench introduces a novel, outcome-oriented framework for evaluating video generation models. Unlike traditional benchmarks that prioritize frame-by-frame consistency or intermediate step accuracy, this approach focuses on Semantic Task Completion. The core objective is to determine whether a generated video successfully achieves a specified outcome while maintaining high-level semantic grounding relative to a reference image.
To facilitate this, the authors provide: * SemComp-Data: A curated dataset spanning six distinct domains, featuring reference images, instructions, and outcome-centric video clips. * SemComp-Bench: A structured evaluation protocol utilizing Vision-Language Models (VLMs) to assess performance via two key metrics: * OA Score (Outcome Achievement): Measures the successful completion of the intended task. * GR Score (Generation Reliability): Measures the semantic grounding and task-relevant consistency.
To facilitate this, the authors provide: * SemComp-Data: A curated dataset spanning six distinct domains, featuring reference images, instructions, and outcome-centric video clips. * SemComp-Bench: A structured evaluation protocol utilizing Vision-Language Models (VLMs) to assess performance via two key metrics: * OA Score (Outcome Achievement): Measures the successful completion of the intended task. * GR Score (Generation Reliability): Measures the semantic grounding and task-relevant consistency.
Initial experiments indicate that while current video generation models are advancing, achieving both precise task outcomes and robust semantic grounding remains a significant challenge.
Initial experiments indicate that while current video generation models are advancing, achieving both precise task outcomes and robust semantic grounding remains a significant challenge.
Authors
- Keyu Tu
- Zhuowei Chen
- Mengqi Huang
- Yuxin Wang
- Jiahao Zhu
- Zhendong Mao
- Yongdong Zhang
- Keyu Tu
- Zhuowei Chen
- Mengqi Huang
- Yuxin Wang
- Jiahao Zhu
- Zhendong Mao
- Yongdong Zhang
Access & Resources
Citation
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For citation purposes, please refer to the arXiv landing page for BibTeX and other reference formats.