Breakthroughs in AI Video Generation Technology and Its Integration with Web3

One of the most significant recent advancements in the field of AI is the breakthrough in multimodal video generation technology. This technology has evolved from generating videos from a single text to integrating text, images, and audio for comprehensive generation capabilities.

Several notable technological breakthroughs include:

1. An open-source EX-4D framework developed by a technology company can convert ordinary videos into free-view 4D content, with a user acceptance rate of 70.7%. This technology makes it possible to generate multi-angle viewing experiences from single-view videos, significantly simplifying the complex processes of traditional 3D modeling.

2. An AI platform's "Hui Xiang" feature claims to generate a "movie-level" video that is 10 seconds long from a single image. However, the authenticity of this promotion still needs further verification.

3. The Veo system developed by a renowned AI research institution can synchronously generate 4K videos and environmental sound effects. This technology overcomes the challenges of matching video and audio, achieving audio-visual synchronization in complex scenes.

4. A certain short video platform's ContentV technology has 8 billion parameters and can generate 1080p videos in 2.3 seconds, with a cost of 3.67 yuan per 5 seconds. Although cost control is quite good, there is still room for improvement in handling complex scenarios.

These technological breakthroughs are of great significance in terms of video quality, generation cost, and application scenarios. From a technical perspective, the complexity of multimodal video generation is exponential, involving the processing of massive amounts of pixel data, ensuring temporal coherence, audio synchronization, and maintaining 3D spatial consistency. Currently, these complex tasks are being achieved through modular decomposition and the collaborative efforts of large models.

In terms of cost, it is backed by the optimization of the reasoning architecture, including hierarchical generation strategies, cache reuse mechanisms, and dynamic resource allocation. These optimizations significantly reduce the cost of video generation.

In terms of application, AI technology is revolutionizing the traditional video production process. Video production, which originally required a large amount of equipment, space, manpower, and time, can now be completed in a short period of time through AI, achieving effects that are difficult to attain with traditional filming. This transformation may reshape the entire creator economy.

So, how are the advancements in Web2 AI technologies related to Web3 AI?

First, the demand structure for computing power has changed. Multimodal video generation requires a diversified combination of computing power, creating new opportunities for distributed idle computing power.

Secondly, the demand for high-quality data labeling is increasing. Generating professional-grade videos requires precise scene descriptions, reference images, audio styles, and other professional data. The incentive mechanisms of Web3 can attract professionals to provide high-quality data materials.

Finally, AI technology is evolving from centralized large-scale resource allocation to modular collaboration, which itself represents a new demand for decentralized platforms. In the future, computing power, data, models, and incentive mechanisms may form a self-reinforcing ecosystem that promotes the deep integration of Web3 AI and Web2 AI scenarios.