Conventionally, video production has required specialized technical skills and an enormous number of man-hours. Regardless of personal videos, businesses have had to rely on professional creators for video content. In recent years, with the rapid evolution of video generation AI technology, the accuracy of video generation has improved, and utilization in business use, such as AI-powered commercials and social media advertisements, is increasing.
In this article, we explain in detail the mechanism of video generation AI, the types of major tools, the steps for AI development, business use cases, and points to note. It is designed to deepen your understanding of the benefits compared to conventional video production methods and be useful for actual business utilization.
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Video generation AI is a technology that automatically generates realistic videos or animations based on instructions (prompts) through text or data such as existing images and videos. Using deep learning algorithms, it can create diverse footage based on input data, significantly streamlining the conventional video production process.
Specifically, the following is possible:
| Generate video from text | By entering a simple story, footage along the scenario is generated. |
| Generate video from images | You can add movement to still-image characters or dynamically change backgrounds. |
| Generate music videos from images and music | Footage matching the rhythm and atmosphere of the music is created. |
| Generate video from news or social media posts | Videos matching existing news articles or social media posts are generated. |
| Avatar generation | Realistic avatars are generated based on user-entered information and images. |
Since video generation AI can generate diverse video content from simple text or images, it is expected to be used for streamlining and creating new content in even more fields in the future.
Many video generation AIs apply the deep learning technologies listed below:
In recent years, Diffusion models and Transformers have achieved remarkable results, especially in high-quality and consistent video generation. First, the AI learns a vast amount of existing video data and their corresponding text descriptions. Then, when a user enters a text prompt, the AI generates a video that matches the instructions based on its learned knowledge. Similar to image generation AI, the quality of the entered prompt greatly affects the quality of the generated video.
Video generation AI is utilized for operational efficiency and new content production in various fields such as the following:
| Advertising | Generate custom videos or promotional footage in line with brand messages |
| Marketing | Production of product introduction videos or social media videos for campaigns |
| Game Development | Utilized for CG and special effects |
| Education Content Production | Generation of video teaching materials |
| Entertainment | Contribute to user-participation content creation and the realization of interactive experiences |
Demand is particularly rising in corporate promotion, education, and the entertainment industry.
In recent years, video generation AI tools have become more diverse, with tools appearing to meet various needs. Since each has different characteristics, it is important to choose according to production needs. Here, we introduce the major video generation AI tools that are attracting particular attention.
Developed by OpenAI, Sora is a video generation AI that generates video from text. Its main features are that it supports high-quality 1080p video generation and videos up to 60 seconds long. It also allows for advanced natural language processing, enabling it to generate videos faithful to needs even from simple text.
Google DeepMind's Veo is an AI tool specialized in high-quality video generation. It can generate very high-precision videos based on text or images. It is utilized in short video production, high-quality YouTube videos, and advertisement production. It is attracting attention in the industry for quickly generating videos with complex scenes and wide-ranging content. While the current model provided is 720p, Google's announcement states that it theoretically has the ability to generate videos at high resolutions up to 4K.
Adobe Firefly is a video generation AI tool that can generate video from text or video from images. It allows for extensive control over camera zoom, angles, and camera motion, enabling more precise and dynamic video production. Additionally, by linking with Adobe's other powerful toolsets, advanced visual effects and dynamic scenes can be created efficiently. Therefore, it is a useful tool for video creators and marketing teams.
Runway is a video generation AI tool developed by the startup Runway that can generate video from text or images. Its main feature is that it provides an intuitively easy-to-use interface, allowing even beginners to produce advanced footage. It is highly evaluated for its ability to generate high-quality videos with simple input and is utilized in a wide range of fields such as advertising, social media content, and educational content.
Pika is a video generation AI tool with strengths in 3D animated characters. It can generate realistic and attractive 3D animated character movements based on text or images and is widely applicable in animation production and game development. Since animation character design and movements can be generated in a short time, improvement in animation production efficiency can be expected.
Video generation AI improves the video production process and brings many benefits to business. Here, we focus on three major benefits and introduce how it supports business efficiency and creativity.
Conventional video production takes an enormous amount of time and effort because many steps—such as preparation of the shooting environment, arranging models, correcting filmed footage, adjusting effects, and synchronizing audio—are performed manually. Video editors need to spend time working manually, which is a heavy burden especially in mass content production. On the other hand, video generation AI can automate many of the production and editing tasks that require specialized skills. As a result, processes that take time manually can be implemented in a short period, and large volumes of video content can be produced quickly.
Video production incurs high initial investments and operating costs for dedicated studios, cameras, lighting equipment, and editing software. Therefore, when SMEs or startups with limited production budgets produce video content, costs become a major barrier. Implementing video generation AI significantly reduces these initial investments and operating costs. Furthermore, high-quality video production can be realized while reducing casting for actors or models and labor costs for professional filming and editing staff to the minimum required. Therefore, even SMEs with limited budgets can produce high-quality footage and strengthen their competitive edge in the video market.
In conventional video production, the scope of expression was limited by factors such as technical constraints, budget, and time. On the other hand, video generation AI enables new direction and creative content expression not bound by conventional frameworks. For example, it is possible to incorporate special effects automatically generated by AI or footage that changes in real-time. Through the utilization of video generation AI, unique and attractive content can be provided, leading to differentiation against competitors' video content and advertisements.
Video generation AI is utilized in various business fields and brings efficiency. Actual use cases are introduced below.
CyberAgent, Inc. actively utilizes talents generated by AI in its advertising business. Advertising campaigns using AI talents are deployed across a very wide variety of media such as Rakuten Group video advertisements, brand advertisements, and outdoor advertisements. In the case of the Rakuten Group, AI talent selection and expression are automatically generated based on actual advertisement delivery data. Person expressions such as the AI talent's face, background, costume, and pose are finely adjusted for each advertisement group, producing creatives that cast the AI talent most suitable for the target. CyberAgent is also working on voice generation technology for AI talents, such as replicating the voices of real-life celebrities or generating natural voices matching the AI talent.
QUICK Corp. has started providing an original video distribution service where fictional casters (avatars) generated by AI read news and information. This service allows for the distribution of programs based on photos of actual people, as if the person themselves were speaking. Specifically, the AI collects various information such as economic information, news, and weather/people-flow data, summarizes it into a manuscript, and automatically generates it with audio and video supporting multiple languages. It can be utilized in a wide range of fields, such as new products for businesses or local governments, notices to residents, and Investor Relations (IR) for financial results information.
Methods for implementing video generation AI include utilizing existing services and using cloud-based AI platforms. Here, we introduce the benefits and drawbacks of each. Note that while there are advanced methods such as building a video generation system in-house using open-source video generation AI models or related libraries, we will not cover the details in this article.
Implementation of video generation AI is possible by using SaaS platforms or marketing tools that possess video generation functions. For example, design tools like Canva are equipped with simple video editing and generation functions. Special-purpose video SaaS, such as automatic advertising video generation tools or training video creation platforms, also fall under this implementation method. Utilizing existing services has the following benefits:
Since it is provided as additional functions to existing platforms, costs are low because infrastructure construction and development are unnecessary. Account registration and settings are simple, and you can start using it immediately. Utilizing existing services has the following drawbacks:
Functions and customizability may be limited. Also, since they are relatively new tools, pay attention to the possibility of being affected by service termination or price changes by the vendor. In particular, compared to other implementation methods, it is an effective option as a first step because it can be started easily.
Implementation is also possible by using APIs of video generation AI models provided by many vendors and incorporating video generation functions into internal cloud platforms or applications. This is suitable when there is talent with a certain level of AI comprehension in-house. It is effective when seeking advanced video generation functions tailored to specific needs or when prioritizing integration with existing systems. Customization according to internal needs is possible, seamless integration with internal cloud environments or applications is available, and advanced original video generation workflows can be built by combining multiple APIs. By additional learning of unique video data, it is also possible to generate videos using unique characters or backgrounds. Drawbacks include the need for talent with expertise in API integration and cloud environment construction/operation, and implementation costs.
Video generation AI has several technical issues and ethical risks. Here, major challenges and their countermeasures are introduced.
Videos generated by AI may involve issues of portrait rights, and it is important to check the terms of use of each service when considering commercial use. In particular, there is a concern that legal trouble may arise if videos of characters similar to the faces or figures of real people are generated without permission. However, problems can be avoided by selecting highly reliable video generation AI models and performing data management that considers portrait rights and privacy.
Some current video generation AIs still find it difficult to realize high resolution or natural movement. In particular, consistency between frames and the accuracy of detail reproduction are insufficient. Also, the content of generated videos is not always accurate. Therefore, content verification is necessary in fields where high accuracy is required, such as education, medicine, and reporting. It is also important to select AI models that put safety at the forefront. For example, OpenAI is taking safety measures such as the following in Sora to prevent the generation of low-quality or easily abused video content:
Since publishing videos lacking ethical consideration can lead to a loss of corporate credibility or legal issues, it is necessary to build a system where humans monitor output internally as well.
By utilizing video generation AI, dynamic footage can be automatically generated from still images or simple text prompts, leading to operational efficiency and production cost reduction in conventional video production processes. On the other hand, care is required when using it regarding ethical and legal challenges, such as video generation involving portrait rights or biases. In particular, when generating content that reflects real people or specific cultural backgrounds, rights relationships must be managed firmly.