Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Models

Understand

The rapid advancement in text-to-video (T2V) generative models has enabled the synthesis of high-fidelity video content guided by textual descriptions.

• Despite this significant progress, these models are often susceptible to hallucination, generating contents that contradict the input text, which poses a challenge to their reliability and practical deployment.
• To address this critical issue, we introduce the SoraDetector, a novel unified framework designed to detect hallucinations across diverse large T2V models, including the cutting-edge Sora model.
• Our framework is built upon a comprehensive analysis of hallucination phenomena, categorizing them based on their manifestation in the video content.