Core Viewpoint - The rapid iteration of AI video models has created a landscape where users can easily generate videos, but practical application remains a challenge for ordinary users [2][3][4]. Group 1: User Needs and Model Evaluation - Many users require clear narratives, reasonable actions, and smooth visuals rather than complex effects [4][6]. - The evaluation focuses on whether these models can solve real problems in practical applications, particularly for novice content creators [5][7]. - A series of assessments were designed to test the models' capabilities in real-world scenarios, emphasizing practical video content creation [8][9]. Group 2: Model Selection and Testing - Eight popular video generation models were selected for testing, including Veo3, Hailuo02, and Jimeng3.0, which represent the core capabilities in the current video generation landscape [11]. - The testing period was set for July 2025, with specific attention to the models' performance in generating videos from text prompts [11]. Group 3: Evaluation Criteria - Five core evaluation dimensions were established: semantic adherence, physical laws, action amplitude, camera language, and overall expressiveness [20][25]. - The models were assessed on their ability to understand prompts, maintain physical logic, and produce coherent and stable video outputs [21][22][23][24][25]. Group 4: Practical Application and Limitations - The models can generate usable visual materials but are not yet capable of producing fully deliverable commercial videos [57]. - Current models are better suited for creative sketch generation and visual exploration rather than high-precision commercial content [65]. Group 5: Future Directions - Future improvements may focus on enhancing structural integrity, semantic understanding, and detail stability in video generation [60][61][62]. - The rise of image-to-video models may provide a more practical solution for commercial applications, bypassing some of the challenges faced by text-to-video models [62].