Vizard Agent

How to make two photos talk to each other in one video

Last updated 2026-10-10 · 2 min read

Upload both photographs and write what each person says. Vizard Agent casts a voice for each, generates the two speaking shots separately, checks the frames and the audio of each before joining them, and builds the captions from a transcript of the assembled cut rather than the script.

What do you type into Vizard Agent?

Write it as a two-shot script. Vizard Agent generates one shot per speaker, so describing what happens in each shot (who is on camera, what they say, how the frame moves to the other) maps directly onto how the video gets built. Write the handover explicitly, because how the second person enters is what connects the two shots.

Prompt

Shot one: the person in photo one says "[line]" to camera. Then walk into frame and bring in the person from photo two, who says "[line]". Voices: [descriptions]. Around [15] seconds.

What does Vizard Agent actually do?

A two-photo dialogue is two separate generations joined by a transition, not one video with two people in it. Each shot has to hold on its own, so each is checked twice, once for the picture and once for what the audio actually says, before the two are joined. In the session behind this article it:

When does this not work well?

Making two real people appear to speak to each other on camera is highly sensitive. Vizard Agent will build it carefully, and every question about whether it should be built at all sits with you. Use only photographs you have the right to use, never script a real person saying something they did not, and do not present it as real footage.

Common questions

Can Vizard Agent use my own voice recording? Yes. Vizard Agent clones from the audio you supply rather than generating a voice from scratch.

How many characters can talk? Two is the practical limit, since each speaker is generated as their own shot.

Why transcribe the cut instead of using the script? Because generated speech does not follow a script's punctuation exactly. Transcribing what was actually said gives timings that match the audio to the word.