Image-to-video vs text-to-video: which produces better AI video results?

Image-to-video produces more realistic, more consistent results than text-to-video, because a reference image locks color, contrast, composition, and look before generation starts. Text-to-video works for exploratory wide environment shots where nothing needs to match. For any shot that has to cut against real footage, generate from an image.

Start from an image whenever the AI shot needs to match something — your practical footage, a previous shot, or a locked visual style. Upload screenshots or stills from your own shoot as visual references before generating, so the output inherits your material's color, contrast, and look instead of the model's defaults. One documented production anchored its AI shots this way: the practical pool was only 6x6 feet — too small to hide the set edges — so the filmmaker uploaded a screenshot of the actor emerging from the water and generated wide ocean and shoreline shots around it, matching the real footage closely enough to intercut. As Alex Arfaoui put it after that shoot: "You definitely get so much more out of AI when you actually use your own footage as the references."

Text-to-video earns its place on shots with no matching constraint — establishing wides, abstract inserts, environments you never filmed. Keep those shots wide: AI-generated characters break down in close-ups where the character has to act, so pull out rather than push in. Text-only prompting also front-loads iteration; expect a conversational back-and-forth rather than a single perfect prompt, and pin a generation you like and ask for variations — "create another one like it, but change X, Y, and Z" — instead of re-rolling from scratch.

Whichever input mode you use, consistency compounds within a session. Once the first shot establishes the look, tone, and effect, subsequent shots in the same scene generate faster and more accurately — in one production, the second shot in a scene landed correctly on the very first generation because the visual language was already established.

Model choice matters more for image-to-video than text-to-video. Seedance 2.0 reference-to-video carries character and environment context across clips, while Veo and Kling handle text-prompted motion and multi-shot sequences well. Inside invideo you don't have to pick a platform per mode — the invideo agent has all of these models and routes each shot to the right one based on whether you're feeding it a reference image or a text prompt.

The honest verdict: text-to-video for speed and exploration, image-to-video for anything that has to look real or match. Most finished films use both — text prompts to find the shot, then a still or screenshot to lock it.

Watch some of these to see what works for you:

You definitely get so much more out of AI when you actually use your own footage as the references. You can make it look very close to something that looks real.

— Alex Arfaoui, independent filmmaker