Meituan Open-Sources LongCat-Video-Avatar 1.5

In June 2026, Meituan's technical team announced the open-source release of LongCat-Video-Avatar 1.5. According to the company, the release moves digital-human technology from an experimental research stage toward more robust commercial use. For anyone working in content and customer service, the news matters less for the headline and more for the word "open-source": a video-generated avatar stops being a vendor's closed product and becomes code that any team can run, audit, and adapt.

It is worth separating fact from launch-day enthusiasm before drawing conclusions.

What Meituan Announced

Meituan is the Chinese delivery and local-services giant, and LongCat is the model line from its research team. According to the company, LongCat-Video-Avatar 1.5 generates video of a digital human — an avatar that speaks and moves from a single image or reference — and now ships under an open license, which allows study, modification, and third-party use without depending on a paid API.

The central angle of the announcement, in Meituan's own words, is the move from "cutting-edge research" to "robust commercial application." That is a vendor's claim about its own work. Real robustness is measured in production, with real cases, not in a release. So the honest path is to treat the technology as promising and available — and to verify the quality in your own workflow before promising anything to a client.

Why Open Source Changes the Game

When an avatar model is open, three things change in practice for anyone producing content or building customer service.

First, cost and dependency. Instead of paying per minute of video generated on a platform, the team can run the model on its own infrastructure. That trades a variable bill for the cost of machines and of people who know how to operate them — not always cheaper, but more under your control.

Second, privacy. Running locally means a face, voice, and script do not have to pass through third-party servers. For customer service handling sensitive data, that matters.

Third, adaptation. Open source is code you inspect. You can understand what the system does, integrate it into your pipeline, and tune it to your case, instead of accepting a closed box.

Where the Digital Human Actually Helps

There are concrete uses today. An avatar can present the same information across several short videos without a new shoot for every variation — useful for FAQs, internal training, and standardized answers on social channels. In customer service, it can give a consistent face to an assistant that already answers by text, making the interaction less cold.

What grounds this at 10Dobro is the thesis we always come back to: the tool multiplies what a good team already delivers; it does not replace it. The avatar does not write the right script, does not decide the brand's tone, and does not guarantee the information is correct. Your team does that. The model handles the video-production step; the judgment stays human.

The Limit: Transparency Is Not Optional

Digital-human technology carries a risk that grows alongside its quality. The more natural the avatar, the easier it is to confuse the audience about whether they are talking to a person or a system. Hype has no place here; a rule does.

The rule we adopt is simple. When a video is generated by an avatar, that is clear to whoever watches. When a customer-service flow uses an assistant with a synthetic face, the user knows it is a system. Not as an aesthetic requirement, but because responsible use of AI starts with not deceiving anyone. A well-made avatar in the service of clear information is a tool; an avatar used to simulate a real person without notice is a problem — ethically, and depending on the context, legally.

What This Means for 10Dobro

The opening of LongCat-Video-Avatar 1.5 widens the range of video-production tools available to test. For 10Dobro, which pairs audiovisual work with AI engineering, this falls under the same standard as always: we assess the quality in our own workflow, measure the real result, and only bring to a client what holds up in practice.

An open model is a chance to learn and to reduce vendor lock-in — not a shortcut to a promise. The value is not in the avatar itself, but in using it with the right script, human oversight, and transparency with the audience. That is how a good team turns a technical novelty into content and customer service that actually work.