AI video generation technology has made significant breakthroughs, and multimodal integration has become a new trend.

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

Several notable examples of technological breakthroughs include:

1. The EX-4D framework open-sourced by a technology company can convert ordinary videos into free-view 4D content, with a user approval rate of 70.7%. This technology enables AI to automatically generate viewing effects from any angle without the need for a professional 3D modeling team.

2. A certain internet giant's "Hui Xiang" platform claims to be able to generate a "movie-quality" video in 10 seconds from a single image. The actual effect will be verified after the Pro version update in August.

3. The Veo technology from a certain AI research institution has achieved synchronized generation of 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. A certain short video platform's ContentV technology has 8 billion parameters and can generate 1080p video in 2.3 seconds at a cost of 3.67 yuan per 5 seconds. Although the cost control is quite good, there is still room for improvement in the generation quality of complex scenes.

These technological breakthroughs have significant implications for video quality, production costs, and application scenarios:

1. In terms of technical value, the complexity of multimodal video generation is growing exponentially. It requires handling single-frame image generation (approximately 10^6 pixels), ensuring temporal coherence (at least 100 frames), audio synchronization (10^4 samples per second), and 3D spatial consistency. Now, this complex task can be achieved through modular decomposition and collaboration of large models, such as breaking down the task into modules like depth estimation, viewpoint transformation, temporal interpolation, and rendering optimization.

2. In terms of cost reduction, it mainly benefits from the optimization of the inference architecture, including hierarchical generation strategies, cache reuse mechanisms, and dynamic resource allocation. These optimizations have enabled a certain short video platform to achieve a low-cost video generation of 3.67 yuan/5 seconds.

3. In terms of application impact, AI technology is revolutionizing the traditional video production process. In the past, a 30-second advertisement might cost hundreds of thousands to produce, but now it only requires a prompt and a few minutes of waiting time. This not only lowers the technical and financial barriers but also achieves perspectives and special effects that are difficult to accomplish with traditional filming, potentially leading to a reshuffling of the creator economy.

The development of these Web2 AI technologies also has an important impact on Web3 AI:

1. The change in the structure of computing power demand has created new opportunities for distributed idle computing power, fine-tuning models, algorithms, and inference platforms.

2. The demand for data labeling has increased, creating new opportunities for photographers, sound engineers, 3D artists, and others to provide professional data materials.

3. 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.