Why are Chinese models leading the way in AI video?

It wasn't until ByteDance's Seedance 2.0 gained widespread popularity that many people truly realized for the first time that Chinese models in the AI video track seem not only to be catching up but also leading the way.

Seedance 2.0 didn't gain popularity by stunning with a single frame but brought about a more subtle yet profound change, that is, AI videos have become something like an industrial product that can be stably delivered for the first time.

The combination of multi - modal input, automatic camera movement, and long - term consistency means that creators can avoid the pain of repeated attempts and instead promote a reusable production process.

However, if we look back in time, we'll find that the leading position of Chinese companies in AI videos didn't happen suddenly.

Actually, even earlier, Chinese models had already gained a clear leading edge in the field of AI videos.

For example, in April last year, Kuaishou's Keling 2.0 had a win - loss ratio of 367% compared to Sora in text - to - video generation, leading comprehensively in character consistency, generation stability, and reproducibility, and was the first to achieve commercially viable AI video production capabilities.

The stability of AI videos is very important, including whether characters can remain consistent, whether the picture will collapse midway, and whether the generation results can be repeatedly reproduced.

These indicators precisely determine whether a video can enter real - world production.

Later, we can see that a group of Chinese companies continued to advance along the same path.

ByteDance continuously strengthened the narrative and camera logic in the Seedance system, and some smaller startup teams even embedded video generation directly into the workflows of e - commerce, advertising, and game user acquisition.

Putting these phenomena together, we'll reach an easily overlooked conclusion:

The Chinese models' phased lead in AI videos isn't about making the models smarter but about solving video - related problems as engineering issues earlier.

To understand this, we must trace back to the origin of the AI video generation methodology.

As early as 2015, AI researchers proposed an approach that seemed to take a detour:

Since it's very difficult to directly generate complex data, can we first "destroy" real data step by step into noise and then, through training and learning, restore the noise back to the real world step by step?

This approach originated from probability modeling and statistical physics and later became the origin of the Diffusion model, which gradually dominated the field of image and video generation after being introduced into deep learning.

Diffusion didn't truly become mainstream until after 2020.

With the improvement of computing resources and the maturity of training methods, this approach demonstrated strong stability and detailed expressiveness in image generation.

It can be said that even today, whether it's images or videos, the high - quality and stable generation effects almost all rely on Diffusion at the underlying level.

Diffusion is naturally good at one thing: making things look realistic, but that's all.

Even if it's extremely sensitive to light, texture, and style, it doesn't truly understand the order and causality before and after the recombination of things.

This is why early AI videos often presented a strange sense of fragmentation: single frames were delicate, but when connected, they were like a dream, with characters not being exactly the same before and after, and actions lacking continuity. Because its underlying logic is a patchwork of entropy increase and then entropy decrease.

Meanwhile, another technical route was maturing rapidly, which was the well - known Transformer architecture that became popular along with GPT. It doesn't solve the problem of generation but the problem of relationships.

For example, how to align information, how to understand the overall time sequence, and how to capture long - distance dependencies. In terms of capabilities, Transformer is more about understanding the structure rather than producing images like Diffusion.

Thus, a key division of labor gradually became clear.

Transformer is good at planning the structure and sequence, while Diffusion is good at actually generating the images.

The problem is that this division of labor wasn't systematically utilized for a long time.

For a long time, overseas teams tended to continuously challenge the upper limit of Diffusion when working on AI videos.

For example, they pursued longer durations, more complex worlds, and more realistic physical effects.

The achievements were indeed quite impressive. For instance, Sora demonstrated the great potential of the model in understanding the real world.

However, the costs of this route are very clear: high generation costs, high failure rates, and poor reproducibility. It's more suitable for showcasing the future rather than supporting today's production.

In contrast, Chinese model teams took a less prominent but more practical path.

They may have realized earlier that the core difficulty of videos doesn't lie in whether they can be generated but in whether they can be completed.

Who appears first, how the camera moves, when to switch perspectives, and which details must remain consistent - these implicit processes that highly rely on experience in traditional film and television were pre - disassembled into the model's constraints.

In this system, Transformer no longer undertakes the grand mission of "understanding the world" but is responsible for planning the structure and rhythm of the video;

Diffusion is no longer required to play freely but to complete specific images under clear instructions.

Under this methodology, a video is no longer regarded as an artistic miracle but as a production line where the success rate needs to be controlled.

This goal of solving problems rather than simply pushing the upper limit is more similar to an engineering logic.

In fact, the core capabilities of the Chinese Internet in the past decade or so have been concentrated on the extreme optimization of content production pipelines.

Industries such as short - form videos, e - commerce live streaming, information - flow advertising, and game user acquisition have long followed a similar logic, which is to decode a large amount of data to calculate posterior probabilities and then disassemble them into standard components for replication according to creative needs.

When the same idea was introduced into AI videos, Diffusion is no longer the dominant part of the generation model but a key component in the industrial process.

The significance of Seedance 2.0 and similar products lies in pushing this route to a new stage.

When they can make the path from "prompt - generation - finished product" stable enough to be used as a daily tool, it still represents an emerging moment in terms of user value.

It must be admitted that in the cognitively intensive field of large language models, Chinese models are still catching up overall;

However, under the guidance of an engineering mindset, Chinese models are more likely to achieve phased leadership in the "process - intensive" field of AI videos.

Because the former depends on the knowledge boundary and the upper limit of reasoning, while the latter depends on engineering judgment, efficiency control, and large - scale implementation capabilities.

When Diffusion and Transformer are correctly divided in labor and organized into a reusable production line, AI videos will no longer be a technological wonder but a real industrial capability.

It's precisely in this regard that Chinese models have taken the lead.

This article is from the WeChat official account "All - Weather Technology" (ID: iawtmt), author: Song He. It is published by 36Kr with permission.