Since 2023, AI video generation technology has undergone a leapfrog evolution from laboratory demonstrations to commercial productization. From the first generation of cultural video models in Runway Gen-2, to the emergence of OpenAI Sora, and to the intensive iteration of domestic products such as Ke Ling and Pika, AI video generation is reshaping the production mode of content creation.
##From Wensheng Video to Multimodal Understanding
Early AI video generation models (2022-2023) mainly relied on one-way text to video generation logic: when a user inputs a text description, the model converts it into a few second short video clip. Runway Gen-2 and Pika 1.0 represent the highest level of this stage, capable of generating 3-4 second coherent videos, but with issues such as small action amplitudes and poor character consistency.
In February 2024, OpenAI released the Sora model, taking AI video generation to new heights. Sora is based on the DiT (Diffusion Transformer) architecture and can generate high-quality videos up to 60 seconds long, demonstrating a preliminary understanding of the physical world - object motion trajectories follow physical laws, and light and shadow changes are natural and smooth. More importantly, Sora demonstrated multimodal understanding ability: not only can she understand textual descriptions, but she can also perform dynamic processing on static images.
Since 2025, AI video generation has entered the stage of "multimodal narrative". Representative products such as Runway Gen-3 Alpha and domestic Ke Ling 1.5 support mixed input of text, images, and video clips. Users can control the composition and movement of videos through keyframes, achieving a leap from "generating clips" to "telling stories".
##Core technology breakthrough
The core technological barriers for AI video generation are reflected in three aspects:
Firstly, spatiotemporal modeling. Video is three-dimensional data (two-dimensional space+one-dimensional time), and the model needs to understand both the spatial structure and the laws of temporal evolution. The introduction of 3D VAE and Causal Attention mechanisms enables the model to efficiently handle temporal dependencies between video frames.
Secondly, consistency in long videos. How to make AI generated videos maintain consistency in characters, scenes, and styles within tens of seconds is currently the biggest technological challenge. The latest VideoPoet and Streaming T2V models effectively alleviate this problem through a layered generation strategy - first generating keyframes and then filling in transition frames.
Thirdly, understanding the physical world. The interaction of objects, fluid motion, and changes in light and shadow in the generated video need to conform to physical laws. The combination of large-scale video data pre training and physical simulation systems is gradually improving the physical knowledge ability of models.
##Application scenarios and market patterns
The commercial application of AI video generation technology is rapidly expanding. In the field of film and television production, AI assisted pre visualization has entered the mainstream workflow - directors can use AI to quickly generate visual previews of storyboard scripts, significantly reducing pre production costs. According to statistics, production teams that use AI assisted preview technology have reduced the average pre production cycle by 40%.
In the field of advertising and marketing, AI video generation is changing the way creative production is done. Brand owners can input product information and style requirements, and AI automatically generates multiple versions of advertising videos for A/B testing. In the first quarter of 2026, the global AI video advertising market has reached $1.2 billion and is expected to exceed $5 billion for the whole year.
In the field of social media, platforms such as TikTok and Instagram have integrated AI video generation capabilities, allowing ordinary users to directly generate short video content through text descriptions. This trend is lowering the threshold for video creation, making 'everyone is a creator' a reality.
The competition in the domestic AI video industry is also fierce. Kwai Keling has gained wide attention with its excellent Chinese style scene generation ability. ByteDance's Jimeng AI focuses on short video and e-commerce scene video generation, while Alibaba Tongyi Wanxiang focuses on long video and film and television effects. Overseas, Runway has developed into the most mature AI video platform, providing not only generation functions but also building a complete video editing workflow that includes editing, special effects, and collaboration.
##Challenges and Prospects
Despite rapid development, AI video generation technology still faces multiple challenges. The high cost of computing power is the biggest constraint - generating a 60 second 1080p video requires several minutes of GPU inference time, which is about 5-10 times more expensive than traditional rendering. In addition, video copyright, deepfake regulation, and the impact on employment for traditional film and television practitioners are all issues that the industry needs to face.
Looking ahead, AI video generation will evolve towards two directions: "real-time generation" and "interactive creation". Real time AI video generation will be applied to live streaming, gaming, and virtual reality scenes; Interactive creation allows users to "edit AI generated content" like editing videos, achieving comprehensive control over the generated results.
When AI video generation truly achieves "what you think is what you get", content creation will usher in a true paradigm revolution - and this may be in the near future.
[Reference source] The content of this article is comprehensively summarized from OpenAI Sora technical report, Runway official blog, Kwai Kering product documentation, and technical analysis publicly released by industry media.