Core Viewpoint - The article discusses the advancement of Text-to-Video (T2V) generation technology, emphasizing the transition from focusing on visual quality to ensuring physical consistency and realism through the introduction of the PhyT2V framework, which enhances existing T2V models without requiring retraining or extensive external data [2][3][26]. Summary by Sections Introduction to PhyT2V - PhyT2V is a framework developed by a research team at the University of Pittsburgh, aimed at improving the physical consistency of T2V generation by integrating large language models (LLMs) for iterative self-refinement [2][3][8]. Current State of T2V Technology - Recent T2V models, such as Sora, Pika, and CogVideoX, have shown significant progress in generating complex and realistic scenes, but they struggle with adhering to real-world physical rules and common sense [5][7]. Limitations of Existing Methods - Current methods for enhancing T2V models often rely on data-driven approaches or fixed physical categories, which limits their generalizability, especially in out-of-distribution scenarios [10][12][18]. PhyT2V Methodology - PhyT2V employs a three-step iterative process involving: 1. Identifying physical rules and main objects from user prompts [12]. 2. Detecting semantic mismatches between generated videos and prompts using video captioning models [13]. 3. Generating corrected prompts based on identified physical rules and mismatches [14] [18]. Advantages of PhyT2V - PhyT2V offers several advantages over existing methods: - It does not require any model structure modifications or additional training data, making it easy to implement [18]. - It provides a feedback loop for prompt correction based on real generated results, enhancing the optimization process [18]. - It demonstrates strong cross-domain applicability, particularly in various physical scenarios [18]. Experimental Results - The framework has been tested on multiple T2V models, showing significant improvements in physical consistency (PC) and semantic adherence (SA) scores, with the CogVideoX-5B model achieving up to 2.2 times improvement in PC and 2.3 times in SA [23][26]. Conclusion - PhyT2V represents a novel, data-independent approach to T2V generation, ensuring that generated videos comply with real-world physical principles without the need for additional model retraining, marking a significant step towards creating more realistic T2V models [26].