BRITE: A Benchmark for Reliable and Interpretable T2V Evaluation on Implausible Scenarios 文章

ArXiv CS.CV2026-06-16NEWSen作者: Advait Tilak, Jiwon Choi, Nazifa Mouli, Wei Le

详细信息

来源站点
ArXiv CS.CV
作者
Advait Tilak, Jiwon Choi, Nazifa Mouli, Wei Le
文章类型
NEWS
语言
en
发布日期
2026-06-16

摘要

arXiv:2605.00873v2 Announce Type: replace-cross Abstract: The rapid advancement of photorealistic Text-to-Video (T2V) generation brings in an urgent need for up-to-date evaluation methods. Existing benchmarks largely overlooked implausible scenarios and do not measure audio-visual alignment. We introduce BRITE, the first framework that unifies (1) implausible prompting, (2) fine-grained assessment of audio-visual consistency, and (3) QA-based interpretable evaluation into a comprehensive T2V benchmark. Unlike fully automated Multimodal LLM-based pipelines, which are prone to hallucination and prompt ambiguity, BRITE guarantees reliability through a rigorous human-in-the-loop protocol for benchmark creation. Evaluating five state-of-the-art models (Sora 2, Veo 3.1, Runway Gen4.5, Pixverse V5.

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