Every Type of AI "Generated" Video Ever, Explained

The video explores the spectrum of AI-generated videos, categorizing them based on user control and the opacity of the AI’s processes, from fully AI-generated text-to-video outputs to more controlled methods like face and body swapping. It highlights advancements in video generation technologies while acknowledging the challenges and inconsistencies that still exist in producing high-quality results.

The video discusses the various types of AI-generated videos, emphasizing the differences in how they are created and the level of opacity involved in each method. The narrator begins by explaining that not all AI videos are created equal, and the term “AI-generated” can be misleading. They categorize these videos on a spectrum based on how much control the user has over the generation process and how much the AI’s workings remain a “black box.” The leftmost end of the spectrum represents fully AI-generated videos, where users input text and receive a video output, while the right side indicates methods that are less opaque and more controlled.

The first type discussed is text-based AI-generated videos, which the narrator considers the most opaque. In this method, users provide text prompts, and the AI generates videos based on that input. This process is seen as a leap of faith, as users have minimal control over the final output. The narrator mentions the potential for future advancements, such as OpenAI’s proposed world simulator, which could create realistic representations of the world. However, current technology often leads to inconsistent results, with the quality of generated videos decreasing after the initial frame.

Moving slightly to the right on the spectrum, the video introduces video-to-video generation, where an AI model paints over a base video. This method improves upon text-to-video by adding a temporal component, allowing for smoother transitions between frames. The narrator highlights Domo AI as a user-friendly platform for video-to-video generation, which offers various artistic styles. Despite its advancements, the video-to-video method is still considered chaotic and less refined than fully AI-generated videos.

The discussion then shifts to face swap technology, which is more specialized and focuses on morphing faces onto existing footage. While face swaps require less generative knowledge than video-to-video methods, they are still capable of producing convincing results. The narrator also touches on body swap technology, which has gained popularity for creating humorous content. As the video progresses, the narrator explains that AI avatars, which replicate a single person’s likeness, are slightly more advanced than face swaps but still limited in their generalizability.

Finally, the video concludes with a look at face manipulation techniques, which animate images like puppets and can synthesize new views of faces. These methods often utilize driving videos to transfer facial movements to target images. The narrator also mentions AI lip-syncing, which focuses on animating the mouth region and can be applied to both images and videos. Overall, the video emphasizes the blending of these various AI technologies to create innovative and entertaining content, while also acknowledging the ongoing engineering challenges in the field.