Breakthroughs and Future Development of AI Video Generation Technology

Recently, the most notable advancement in the AI field is the significant breakthrough in multimodal video generation technology. This technology has evolved from purely generating videos from text to a full-link generation technology that integrates text, images, and audio.

Several notable cases of this technological breakthrough include:

1. An open-source EX-4D framework from a certain tech company can convert ordinary monocular videos into free-view 4D content, with a user acceptance rate of 70.7%. This technology allows AI to automatically generate viewing effects from any angle, which previously required a professional 3D modeling team to achieve.

2. An AI platform launched the "Hui Xiang" feature, claiming it can generate a "movie-quality" video of 10 seconds from a single image. However, the authenticity of this claim still needs further verification.

3. The Veo technology developed by an AI research institute can achieve the synchronized generation of 4K video and ambient sound. The key to this technology lies in achieving true semantic-level audio-visual matching, overcoming the synchronization challenges 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, at a cost of 3.67 yuan per 5 seconds. Although cost control is good, there is still room for improvement in the generation quality in complex scenarios.

These technological breakthroughs are of great significance in terms of video quality, generation costs, and application scenarios. From a technical perspective, the complexity of multimodal video generation is exponential, involving multiple aspects such as image generation, temporal coherence, audio synchronization, and 3D spatial consistency. Currently, these complex tasks are being achieved through modular decomposition and collaborative work among large models.

In terms of cost, the optimization of the inference architecture, including hierarchical generation strategies, cache reuse mechanisms, and dynamic resource allocation, has significantly reduced generation costs. This makes AI video generation more advantageous in terms of economics.

The impact on application fields is also quite significant. Traditional video production is a capital-intensive industry, whereas AI technology simplifies this process to inputting prompts and waiting for a few minutes, while also achieving perspectives and effects that are difficult to attain with traditional filming. This may trigger a reshuffling of the creator economy, shifting the focus from technical and financial thresholds to creativity and aesthetic ability.

These changes are closely related to Web3 AI:

1. Changes in the demand structure for computing power may increase the demand for distributed idle computing power, as well as the demand for various distributed fine-tuning models, algorithms, and inference platforms.

2. The demand for data annotation will also increase. Generating professional-grade videos requires precise scene descriptions, reference images, audio styles, camera movement trajectories, and lighting conditions, among other specialized data. The incentive mechanism of Web3 can encourage professionals to provide high-quality data materials.

3. The shift of AI technology from centralized large-scale resource allocation to modular collaboration itself represents a new demand for decentralized platforms. In the future, computing power, data, models, and incentive mechanisms may form a self-reinforcing virtuous cycle, promoting the deep integration of Web3 AI and Web2 AI scenarios.