Breakthroughs in AI video generation technology bring new opportunities to the Web3 field

Recently, there have been remarkable technological advancements in the field of AI video generation. Multimodal video generation technology has evolved from generating videos from a single text to integrating text, images, and audio into a full-link generation. This breakthrough brings new possibilities for creators and businesses.

Several typical cases of technological breakthroughs are worth paying attention to:

1. A framework open-sourced by a certain tech company can convert ordinary videos into free-view 4D content, with a user approval rate of 70.7%. This technology makes it possible to generate multi-angle viewing effects from a single perspective without the need for a professional 3D modeling team.

2. An AI platform claims it can generate a "movie-quality" video of 10 seconds from a single image. The specific effects remain to be verified.

3. A technology developed by an AI research institution can simultaneously generate 4K video and ambient sound. This technology overcomes the challenges of audio-visual synchronization in complex scenes, such as the precise correspondence between walking actions in the footage and the sound of footsteps.

4. An AI model from a short video platform can generate 1080p videos in 2.3 seconds, costing about 3.67 yuan/5 seconds. Although there is room for improvement in complex scenarios, it has already shown competitiveness in cost control.

These technological breakthroughs are significant in terms of video quality, generation cost, and application scenarios. From a technical perspective, the complexity of multimodal video generation is exponential. It not only has to process pixel points of single frame images but also needs to ensure the temporal coherence of the video, audio synchronization, and 3D spatial consistency. Currently, this complex task is being achieved through modular decomposition and the collaborative division of labor among large models.

In terms of cost control, the new technology employs optimization methods such as layered generation strategies, cache reuse mechanisms, and dynamic resource allocation, significantly reducing the cost of video generation.

These advancements have brought a huge impact on the traditional video production industry. AI technology simplifies the complex video production process into prompt input and a short wait, which not only lowers the technical and financial barriers but also achieves effects that are difficult to reach with traditional filming. This could trigger a new round of transformation in the creator economy.

So, how do these advancements in Web2 AI technologies affect the Web3 AI domain?

First, the demand structure for computing power has changed. Multimodal video generation requires a diversified combination of computing power, which creates new demands for distributed idle computing power, various distributed fine-tuning models, algorithms, and inference platforms.

Secondly, the demand for professional data annotation has increased. Generating high-quality videos requires precise scene descriptions, reference images, audio styles, camera motion trajectories, and lighting conditions among other professional data. The incentive mechanisms of Web3 can attract professionals to provide high-quality data materials, thereby enhancing the capabilities of AI video generation.

Finally, the development of AI technology towards modular collaboration has created new demands for decentralized platforms. In the future, computing power, data, models, and incentive mechanisms may form a self-reinforcing positive cycle, promoting the deep integration of Web3 AI and Web2 AI scenarios.