Author: Haotian
Apart from the localization "sinking" of A, the biggest change in the AI track recently is that multi-modal video generation technology has broken through, evolving from previously supporting pure text video generation to a full-chain integrated generation technology of text + image + audio.
Let me casually mention a few technical breakthrough cases for everyone to get a sense:
1) ByteDance's open-source EX-4D framework: Single-view video instantly transforms into free-angle 4D content, with a user recognition rate of 70.7%. In other words, given an ordinary video, AI can automatically generate viewing effects from any angle, which previously required a professional 3D modeling team;
2) Baidu's "Drawing Imagination" platform: Generating a 10-second video from a single image, claiming to achieve "movie-level" quality. However, whether it's exaggerated by marketing packaging needs to be seen after the Pro version update in August;
3) Google DeepMind Veo: Can synchronously generate 4K video + environmental sound. The key technical highlight is the achievement of "synchronization", previously video and audio were two separate systems spliced together, requiring overcoming significant challenges to achieve true semantic-level matching, such as corresponding audio-visual synchronization of walking movements and footstep sounds in complex scenes;
4) TikTok ContentV: 8 billion parameters, generating 1080p video in 2.3 seconds, at a cost of 3.67 yuan/5 seconds. Honestly, the cost control is quite good, but the current generation quality still falls short in complex scenarios.
Why do these cases represent significant breakthroughs in video quality, generation cost, and application scenarios?
1, In terms of technical value breakthrough, the complexity of multi-modal video generation is often exponential. Single-frame image generation involves approximately 10^6 pixels, video requires temporal coherence (at least 100 frames), plus audio synchronization (10^4 sampling points per second), and 3D spatial consistency.
Comprehensively, the technical complexity is not low. Originally, it required a super-large model to handle all tasks. It's said that Sora burned thousands of H100 GPUs to acquire video generation capabilities. Now, it can be achieved through modular decomposition and large model collaborative division of labor. For example, ByteDance's EX-4D actually breaks down complex tasks into depth estimation modules, view conversion modules, temporal interpolation modules, rendering optimization modules, etc. Each module specializes in one task, then coordinates through a coordination mechanism.
2, In terms of cost reduction: Behind this is the optimization of the inference architecture itself, including stratified generation strategies, first generating low-resolution skeletons and then enhancing high-resolution imaging content; cache reuse mechanisms, which is the reuse of similar scenes; dynamic resource allocation, which adjusts model depth based on specific content complexity.
After such an optimization, TikTok ContentV achieves 3.67 yuan/5 seconds.
3, In terms of application impact, traditional video production is a heavy-asset game: equipment, venues, actors, post-production. A 30-second commercial typically costs hundreds of thousands. Now, AI compresses this process to a prompt + a few minutes of waiting, and can achieve perspectives and special effects difficult in traditional shooting.
This transforms the technical and financial barriers of video production into creativity and aesthetics, potentially driving a reshuffling of the creator economy.
The question is, what does this web2 AI technology demand-side change have to do with web3 AI?
1, First, the change in computing power demand structure. Previously, AI competed on computing power scale, with more homogeneous GPU clusters winning. However, multi-modal video generation requires diverse computing power combinations, potentially creating demand for distributed idle computing power and distributed fine-tuning models, algorithms, and inference platforms;
2, Secondly, data annotation needs will strengthen. Generating a professional-level video requires precise scene descriptions, reference images, audio styles, camera movement trajectories, lighting conditions, etc., which will become new professional data annotation needs. Using web3 incentive methods can stimulate photographers, sound engineers, 3D artists to provide professional data materials, enhancing AI video generation capabilities through professional vertical data annotation;
3, Lastly, it's worth mentioning that as AI gradually moves from centralized large-scale resource allocation to modular collaboration, this itself represents a new demand for decentralized platforms. At that time, computing power, data, models, and incentives will combine to form a self-reinforcing flywheel, driving a large integration of web3 AI and web2 AI scenarios.
