{
  "id": 18002,
  "url": "https://arxiv.org/abs/2608.09594v1",
  "title": "Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluation",
  "summary": "Recently, AI-driven video generation has attracted considerable attention. This surge increases the demand for reliable video quality assessment (VQA) metrics to evaluate AI-generated content (AIGC) videos and guide model optimization. Existing studies assess video quality through visual harmony, video-text consistency, and domain-specific alignment, yet lack quantitative metrics for measuring fidelity to physical laws. To address this limitation, we present a novel benchmark that evaluates the",
  "authors": "Yifei Xue, Yuanchen Fei, Hao Zhang, Chenzhi Nie, Tie ji, Yizhen Lao",
  "category": "research",
  "topics": "safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T13:28:07.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18002",
  "original_url": "https://arxiv.org/abs/2608.09594v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}