Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos
Artifact-Bench is a new benchmark for systematically evaluating multimodal large language models (MLLMs) on detecting and analyzing artifacts in AI-generated videos. It establishes a three-level hierarchical taxonomy covering photorealistic, animated, and CG-style videos, and defines three complementary tasks. Experiments on 19 leading MLLMs reveal substantial limitations in artifact perception and reasoning, with many models performing near or below random in challenging settings, and significant misalignment with human perceptual preferences.
[2605.18984] Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos
[Submitted on 18 May 2026]
Title:Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos
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Abstract:Recent video generative models have greatly improved the realism of AI-generated videos, yet their outputs still exhibit artifacts such as temporal inconsistencies, structural distortions, and semantic incoherence. While Multimodal Large Language Models (MLLMs) show strong visual understanding capabilities, their ability to perceive and reason about such artifacts remains unclear. Existing benchmarks often lack systematic evaluation of artifact-aware perception and fine-grained diagnostic reasoning, especially across diverse AI-generated video domains beyond photorealistic content. To address this gap, we introduce Artifact-Bench, a comprehensive benchmark for evaluating MLLMs on AI-generated video artifact detection and analysis. We first establish a three-level hierarchical taxonomy of realism artifacts, covering photorealistic, animated, and CG-style videos. Based on this taxonomy, Artifact-Bench defines three complementary tasks: real vs. AI-generated video classification, pairwise realism comparison, and fine-grained artifact identification. Experiments on 19 leading MLLMs reveal substantial limitations in artifact perception and reasoning, with many models approaching random or even below-random performance in challenging settings. We further observe significant misalignment between MLLM judgments and human perceptual preferences, highlighting their limited reliability as general evaluators for AI-generated video realism.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.18984 [cs.CV]
(or arXiv:2605.18984v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.18984
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yang Shi [view email] [v1] Mon, 18 May 2026 18:04:54 UTC (21,044 KB)
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