Hello everyone! We are quickly approaching the final month of 2025—December. With the onset of early winter, warm clothing and seasonal health management are definitely needed these days.
Today, we'd like to introduce you to the technology and case studies behind generating video from text. Many of you may already be using solutions that create videos through natural language commands.
How about creating daily, repetitive informational videos, such as weather forecasts or stock market updates, using AI? In the case of weather reports, if there are no major variables like special weather advisories, the news is presented with a similar structure and format daily, only the content changes. We believe that by training AI with this familiar, repetitive content, it will be possible to generate high-quality videos with excellent production efficiency. Of course, it will be crucial to reduce viewer reluctance toward AI-generated content.
🎬The Complex Process of Video Production
The ripple effect of a single, well-made video is immense. The public increasingly favors video content, a trend that appears set to continue for the foreseeable future. Until now, producing a video required countless hours and dedication from numerous creators. A video is only truly complete after passing through multiple phases: planning, shooting, editing, and subtitling/dubbing.
Planning: Brainstorming what message to convey and how to structure the story to capture attention.
Shooting: Pre-production setup—including lighting, casting, and location scouting—along with dealing with various variables that arise on set.
Editing: The seemingly endless maze of putting scenes together, selecting effects, and choosing music to enhance overall quality.
Subtitling/Dubbing: Questions arise: What about international viewers? Who handles the translation? Should we use an AI voice or a professional voice actor? How do we synchronize the timing perfectly? The work is endless, the adjustments repetitive!
For videos requiring high quality, such as movies or dramas, the process demands the expertise of numerous specialists and massive budgets. In short, video production was once a field that non-experts couldn't even dream of entering.
🖼️Generative AI Innovation, Starting with Images, Spreading to Video
Image generation technology has seen remarkable advancements recently. With just a few short text prompts, like "a giraffe flying in the night sky," you can instantly create incredibly realistic images that make you do a double-take. Even beyond specialized image generation services like Midjourney or Stable Diffusion, you can easily generate images using simple conversational commands in everyday tools like ChatGPT or Gemini.
<'A giraffe flying in the night sky' generatedby Gemini (left) and ChatGPT (right)>
This generative technology is now expanding its scope into the realm of video. We are entering an era that goes beyond merely creating static images, to one where AI predicts the flow of time and motion to produce videos.
It may be helpful to imagine the scene where text becomes a magic brush, painting movement onto a blank screen.
💡Principles and Procedures for Generating Video from Text
The technology underlying Text-to-Video generation is rooted in the Diffusion Model, which has recently gained significant attention in AI image generation. The generative principles are as follows:
① Text Encoder: The AI receives the user-inputted prompt (e.g., "a giraffe flying in the night sky") as text and converts it into a mathematical code that the system can understand..
② Diffusion Model: Video generation starts not with a blank canvas, but with a screen full of random noise. The model references the converted code and progressively removes this noise until the form of a 'moving picture' emerges.
③ Visual Consistency (Temporal Consistency): While images are static, videos require continuous motion. The AI adds visual consistency (also known as Temporal Consistency) to the image generation process. It predicts and controls the movement of preceding and succeeding frames to ensure the motion is naturally connected, resulting in a finalized moving video.
Based on these principles, the AI generative model produces video content. The procedure is as follows:
① Prompt Input: The user meticulously enters the desired content, style, and atmosphere of the video into the prompt. Naturally, the more detailed the input, the more likely the generated video will align with the user's intent.
② Text-to-Code Conversion: The AI analyzes the user's prompt (text) and translates it into a mathematical code.
③ Video Generation: The code is applied to the noisy screen, creating consecutive frames second by second.
④ Video Completion: The finished video content is outputted, fully aligned with the instructions given in the text.
Although the AI generates videos through this process, the quality of the resulting video may rarely align 100% with the user's intent. Therefore, human experts often refine or supplement the AI-generated content to finalize the end product.
🎯How Can Text-to-Video Technology Be Applied?
The technology for generating video from text presents a significant opportunity for those dealing with text-based content.
For instance, consider newspapers or magazines—media organizations that specialize in text-based content. If they could generate a news briefing video from their periodically written articles in just a few seconds, they would be able to distribute it widely to their readers via their own websites, YouTube, and social media platforms.
This technology can also be useful for content providing daily, repetitive information in similar formats, such as weather forecasts or stock market updates. By pre-training the AI on a specific format, the system can generate high-quality video content using only text information tailored to that format.
<Example Image of 'This Week's Weather' Generated by AI>
So far in Part 1, we’ve provided a brief introduction to the technology behind generating video from text. In the next installment, we will delve into more specific details through practical video generation case studies.
See you in Part 2!
