Model tuning

Kling Image To Video Prompt

Make the motion explicit, keep the reference image stable, and state what must not change during the clip.

Try it here

Mini prompt studio

Change the image summary or motion and copy a focused prompt.

Prompt

Create a Kling-ready image-to-video prompt from the reference image. Subject anchor: an action figure standing on a desk with a city skyline visible through the window. Motion: the figure raises one arm slightly while the camera orbits around the desk. Camera: orbit movement with a clear beginning, middle, and ending. Visual style: cinematic lighting, natural depth of field, controlled color contrast. Constraints: use the provided image as the first frame; keep the main subject visually anchored through the full shot; do not invent new major objects; keep the background layout consistent unless motion is requested; compose for 16:9 and 10s. Model rewrite: Prioritize the subject motion, camera path, and what must stay locked.

Kling playbook

What makes this page different

Use Kling when the main challenge is motion: subject movement, camera path, body geometry, object shape, and background stability.

Best for

Action figures, characters, people, products, and objects that need visible movement.
Orbit, tracking, handheld, or push-in camera paths where geometry must remain stable.
Short social or cinematic clips where the first frame must stay recognizable while motion increases.

Not ideal for

Tiny label-copy changes or text-heavy product packaging where Runway-style concise control is safer.
Loose prompts with several body actions competing in the same few seconds.

Prompt formula

Kling-specific structure

First-frame lock Say exactly which figure, object, pose, desk, window, horizon, or room layout must remain unchanged.
Motion verb Use one strong verb: raises, turns, steps, rotates, reaches, leans, glides, or tracks.
Camera path Describe the path as a physical move: slow orbit around the desk, tracking along the frame, or handheld push-in.
Geometry locks Repeat body proportions, limb count, object silhouette, and background alignment.
Motion negatives Block warped limbs, sliding backgrounds, rubbery surfaces, sudden zooms, and jitter.

Failure modes to block

Arms, hands, wheels, props, or toy limbs bend when the camera moves.
Background geometry slides during orbit or tracking movement.
The subject changes proportions after the first second.

Recommended settings

Use 5s for social hooks and 8-10s for controlled cinematic motion.
Pair orbit or tracking with explicit geometry locks.
Use negative prompts for warped limbs, identity change, sliding background, and camera jitter.

Compared with the other model pages

Kling pages should make the motion path and geometry locks impossible to miss. Seedance pages can be more structured and editorial.

kling image to video prompt

How to tune this model

Kling-oriented prompts should make the subject movement and camera path concrete.
Use lock phrases for character identity, pose, object shape, and background geometry.
Name hand or limb failures when the shot includes articulated subjects.

Desk action figure

Before and after prompt

Before

Kling prompt to animate this toy on the desk.

After

Use the image as the first frame. Preserve the action figure design, desk objects, window frame, and skyline position. Create a 10s 16:9 shot where the figure raises one arm slightly while the camera makes a slow orbit around the desk. Keep the body proportions consistent. Negative prompt: identity change, warped limbs, melting plastic, sliding background, jitter.

Why it works

It makes the orbit specific.
It protects the toy's shape.
It flags limb and background artifacts.

FAQ

Common questions

Can I use the prompt in Seedance, Veo, Kling, or Runway?

Yes. Pick the target model in the generator and it rewrites the same image idea with model-specific emphasis.

Does the tool analyze uploaded images?

The first version works from your written image description, image URL, and motion notes. The upload is for preview while you write.

Why include a negative prompt?

Image-to-video outputs often fail through identity drift, warped text, unstable backgrounds, or jitter. The negative prompt names those failure modes directly.