Technical demo

Your pet has opinions.

Upload a photo, pick something being argued about online, add your own director notes — and get a short video of your pet weighing in. This page documents one generated clip and exactly how it was made.

The clip

720×1280 · 8s · h264 + AAC · 24fps · 3.4 MB

Butcher, on humanoid robots

“That robot is going to be the best-selling product of all time. Every house gets one.”

A bulldog in a podcast booth, mid-argument on a video call. The audio, the mouth movement and the room were all produced in a single generation from a text prompt — no reference photo, no separate lip-sync pass.

Model	google/veo-3.1-lite
Route	OpenRouter video API
Mode	Text-to-video, native audio
Generation time	62 seconds
List price	$0.05 / second at 720p

How it is made

The photo

Breed, coat colour and markings are described in words. Naming the breed explicitly carries the likeness further than image resolution does.

The topic

A real discussion — a thread, a podcast clip, an argument. The claim being made is preserved; only the stakes change.

Director notes

The owner supplies the attitude. Loud and self-important, or flat and unimpressed. This is what makes it sound like their pet.

The render

Scene, performance and the exact spoken line go to the model as one prompt. Audio and mouth movement are generated together.

Notes from the build

Native generation beats lip-sync retrofitting. Post-hoc lip-sync tools are trained on human faces and track landmarks that a muzzle does not have. Generating the animal already talking sidesteps the problem entirely — it is the single most important finding so far.
Describing the breed matters more than image quality. A full-resolution character plate was reconstructed faithfully from a 150×150 thumbnail once the coat and markings were named in words.
Every line is verified, not assumed. Generated audio is transcribed back with Whisper and checked against the script before a clip is accepted.
Each shot gets a clean context. Generating several clips in one conversation caused dialogue to bleed between characters — one had another’s line appended to its own.
Captions are burned in afterwards, not generated. Video models render text unreliably; an overlay pass gives identical typography across every clip and costs nothing to restyle.

Known rough edges

This clip is an honest first pass, not a finished result. The coat came back tan-heavy where the character is meant to be predominantly white, and the video-call grid rendered the same dog in every panel instead of four distinct characters. Both trace to the same cause: this route accepts only a first or last frame, not multiple character references. The fix is to generate each character separately and composite them into a 2×2 grid afterwards, which also frees the bottom frame for branding.