On January 14th, Vidu's open platform officially launched the "one click generation of MV" function. In the era of short videos, "shooting" and "cutting" have become popular skills, but MV is still the most difficult type of content creation to be scaled up: it requires both narrative and punctuation; To achieve both visual consistency and emotional progression; We need both visual style and lyrical expression. In the past, this meant longer production chains, higher labor costs, and stronger professional experience.
Vidu's "one click MV generation" launched on the open platform is not essentially "another template function", but a more radical productization attempt: breaking down MV production into multiple professional roles (director, storyboard, generation, editing and synthesis), and handing them over to multi-agent collaboration to complete, allowing creators to shift from "material handling+timeline labor" to "giving intention, direction control, and aesthetic choices".
Product highlight breakdown: It's not about "generating a video", but "automatically running a production chain"
Change from 'First out of the picture' to 'Understand the music first'
The default path for most video generation tools is: give prompt words → generate images → and then check if they are compatible. The trouble with MV is that rhythm and emotion determine the grammar of the shot. If the system can first understand the structure of the music and then decide on the camera rhythm and paragraph progression, it can transform the "stuttering" from post production labor to pre planning.
This is also the most crucial product logic of "One Click MV": it regards music as the main timeline, allowing the visuals to serve the music, rather than letting the music accommodate the visuals.
Using 'storyboard scripts' to turn creativity into executable camera language
The success or failure of music videos often lies not in a single frame, but between shots: whether the changes in scenery are reasonable, whether the camera movements are consistent, and whether there is an emotional gradient in paragraph switching. Translating 'text creativity' into 'storyboarding instructions' is equivalent to pulling the results back from random generation to' controllable generation '.
You can understand it as: the system is not only responsible for 'drawing', but also for 'how to shoot'.
Style consistency: Pin the "art settings" with reference images
The visual experience of MV is afraid of "drifting": the characters may become fat and thin, the scenes may change from ancient to modern, and the colors may become cold and warm. For creators, reference images are not "aids", but "artistic settings". When the tool supports multiple reference images and treats them as consistency anchors, it can be closer to a "deliverable" fragmented logic: running a complete MV using the same visual language.
Editing and synthesis automation: Handing over the most time-consuming "timeline labor"
The bottleneck of MV production is often not "unable to come up with the picture", but "unable to cut it all": transition stuttering, subtitle synchronization, and rhythm fine-tuning. Productizing these steps means that the creator's energy can shift from "mechanical labor" to "aesthetic judgment": choosing which version to use, deleting which paragraph, and retaining which style.
Who is it suitable for: Three groups of people will benefit first
Music Promotion and Label/Brokerage Team
What they need is "quick production of distributable versions" and the ability to do multi version testing: different art styles, different narratives, different rhythms, which one is easier to spread. End to end MV generation has transformed promotion from "single premium gambling hit" to "multi version parallel trial and error".
Brand Marketing and Content E-commerce Team
Brands are more concerned with "unified visual assets+fast batch output". When the reference image can stably convey the brand's visual language, MV can become a higher density advertising carrier: using music to drive emotions to stay, and using images to unify and strengthen memory points.
Self media, short drama teams, and individual creators
For individuals, the difficulty of MV is never "ideas", but "execution". The value of one click music videos lies in reducing execution costs to an affordable range, allowing individual creators to create something that looks like a team effort, and leaving time for topic selection and aesthetic iteration.
Competitor comparison (not competing on parameters, competing on routes): Vidu is taking the "studio" route
In the "AI Video" race track, common routes can be roughly divided into three categories:
Template/special effects route: Strong in "fast" and "easy to use", suitable for social media hot memes and lightweight content, but the narrative and structure are easily monotonous.
Model generation route: Strong in visual imagination and camera performance, but users often have to repeatedly try prompts and draw cards until they 'come across a usable version'.
End to end production route (Vidu is more like this type): Breaking down the process into directing, storyboarding, generating, editing, and synthesizing, emphasizing the closed-loop completion of "from input to film" rather than the strongest single point capability.
let me put it another way:
Template tools solve the problem of 'I want to quickly create an effect';
Pure generation tools solve the problem of 'I want to generate a cool clip';
End to end MV solves the problem of 'I want to deliver a complete work'.
That's also why 'One click MV' is more like a new product category, not just an upgrade of old features: it regards' structured production of works' as the core issue.
Meaning for the industry: MV may become one of the first large-scale content forms for AI videos
MV is a natural content suitable for automation: its rhythm is constrained by music, emotions are driven by paragraphs, camera language can be templated, and subtitle synchronization can be engineered. As long as the tool can integrate "narrative structure+visual consistency+stuttering editing", MV may become one of the first directions to run large-scale commercial models in end-to-end video generation.
More importantly, these tools will change the division of labor in creation:
The core competency of future creators may shift from being able to edit to being able to direct - providing clearer intentions with less input and making choices and trade-offs with stronger aesthetics.
Conclusion: The real threshold is not in "generation", but in "deliverables"
In the past, many AI video products focused on whether they could be generated, but creators wanted whether they could be delivered. One click MV generation "shifts the focus from the quality of individual video segments to the usability of the complete production chain: structure, rhythm, consistency, subtitles, transitions - these are the key to whether a work can be released, disseminated, and commercially used.
If the first half of video generation is about "making the picture", then the second half is about "making the work into a finished product". Vidu is betting on the latter this time.
