Core Viewpoint - The article emphasizes the capabilities of the Keling 2.1 model in generating first and last frame videos, particularly focusing on image generation and prompt creation, which are crucial for producing high-quality content [1][7]. Summary by Sections Image Acquisition Methods - Three primary methods for obtaining suitable images for first and last frame video generation are discussed: same prompt card drawing, modified prompt card drawing, and using image editing models like FLUX Kontext [8]. - Using the same prompt for card drawing often yields highly similar images, making it ideal for showcase-type videos [9]. - Modifying prompt card drawing allows for the movement or disappearance of main characters or objects by changing parts of the prompt after generating the initial image [12]. - Image editing models enable precise control over images through natural language, allowing for various effects to be added [15]. Prompt Generation for First and Last Frame Videos - The prompts used for generating first and last frame videos are entirely AI-generated, leveraging the enhanced understanding and adherence capabilities of the Keling 2.1 model [27]. - A structured approach to prompt creation is outlined, focusing on analyzing differences between the starting and ending frames and selecting appropriate transition strategies [28][29]. - The article details how to construct specific changes in the visuals, including object transformations, environmental changes, and stylistic variations [37]. Value Creation and Narrative Enhancement - The article suggests that the true value lies in solidifying the process into a template for future projects, enhancing productivity significantly [39]. - It emphasizes the importance of elevating effects into narratives, transforming the approach from mere visual transitions to storytelling, which can significantly increase the perceived value of the videos produced [41].

Core Viewpoint - The article discusses the rise of AI-generated videos, particularly focusing on the use of the Veo3 model, which significantly reduces production costs and allows for the creation of viral content with minimal human input [6][46]. Group 1: AI Video Creation - The introduction of Veo3 has drastically lowered the production costs for AI videos, making it an opportune time for creators to enter this space [6]. - Most viral AI videos are generated with minimal human creativity, relying heavily on AI for concept generation and execution [6][10]. - The process of creating these videos has become almost automated, allowing for the development of video agent products [6][10]. Group 2: Analyzing Viral Videos - The article outlines a method for analyzing successful videos using tools like NotebookLM, which can dissect the structure and content of viral videos [8][9]. - Key elements of successful videos include a "Contrast Engine" that creates humor through unexpected juxtapositions, an "Authentic Format" that mimics real-life recording styles, and leveraging "Shared Knowledge" to connect with the audience [11][12][13]. Group 3: Creative Expansion - The article provides a framework for expanding video ideas by utilizing AI to generate detailed scene descriptions and dialogue based on successful formats [17][21]. - Specific templates for generating prompts for both first-person Vlog and pseudo-interview styles are included, emphasizing the importance of detailed descriptions for effective content creation [29][32]. Group 4: Video Production Process - The article describes the streamlined process for generating videos using Gemini, highlighting the ease of inputting prompts and generating content [37][40]. - Post-production involves simple editing tasks, such as merging clips and adding subtitles, which can be done using common tools like剪映 [44][45]. Group 5: Future of AI Video Production - The article predicts that as AI video production technology continues to evolve, the potential for content creators will expand exponentially, leading to a surge in viral content creation [46].