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
The rapid advancement of photorealistic Text-to-Video (T2V) generation necessitates more sophisticated evaluation methods to keep pace with technological progress and identify limitations.
A robust benchmark for T2V generation, especially one that addresses implausible scenarios and audio-visual consistency, is crucial for improving model reliability and interpretability, directly impacting future AI development and application.
The introduction of BRITE provides a standardized and more comprehensive evaluation framework that can reveal previously overlooked weaknesses in T2V models, potentially reorienting research priorities towards more reliable and interpretable AI.
- · AI researchers in T2V
- · Companies developing T2V applications
- · Users of generated video content
- · AI ethics and safety organizations
- · T2V models relying on superficial evaluation
- · Fully automated Multimodal LLM-based evaluation pipelines
- · Developers ignoring audio-visual alignment
BRITE will force T2V models to improve their handling of complex, implausible scenarios and enhance audio-visual coherence.
Improved T2V reliability and interpretability could accelerate the development of more trustworthy and versatile AI agents and media creation tools.
Higher quality, more controllable T2V generation might lead to new forms of content creation, education, and digital experiences, blurring lines between reality and simulation.
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Read at arXiv cs.AI