Core Viewpoint - The article evaluates the capabilities of nine representative image-to-video AI models, highlighting their advancements and persistent challenges in semantic understanding and logical coherence in video generation [2][7][50]. Group 1: Evaluation of AI Models - Nine models were tested, including Google Veo3, Kuaishou Kling 2.1, and Baidu Steam Engine 2.0, covering both newly launched and mature products [7][8]. - The evaluation focused on real-world creative scenarios, assessing models on criteria such as image quality, action organization, style continuity, and overall usability [9][14]. - The testing period was in August 2025, with a standardized prompt and conditions for all models to ensure comparability [13][9]. Group 2: User Perspectives - Young users, who are not professional video creators, expressed a need for easy-to-use tools that can assist in daily content creation [3][4]. - The evaluation was conducted from a practical and aesthetic perspective, reflecting a generally positive attitude towards AI products [5]. Group 3: Performance Metrics - The models were assessed based on three main criteria: semantic adherence, physical realism, and visual expressiveness [14][21]. - Results showed that Veo3 and Hailuo performed best in terms of structural integrity and visual quality, while other models struggled with semantic accuracy and physical logic [17][21]. Group 4: Specific Use Cases - The models were tested across various scenarios, including workplace branding, light creative expression, and conceptual demonstrations [11][16]. - In the workplace scenario, models were tasked with generating videos for corporate events, while in creative contexts, they were evaluated on their ability to produce engaging and entertaining content [11][16]. Group 5: Limitations and Future Directions - The evaluation revealed significant limitations in the models, particularly in generating coherent narrative sequences and adhering to physical laws in complex scenes [39][50]. - Future developments are expected to focus on enhancing the models' ability to create logically complete segments, integrate into creative workflows, and facilitate collaborative storytelling [53][54][55].