Lesson 5 of 13 · Module 2: AI Video Generation
Image-to-Video: Directing Motion From a Still
Animate one still image into a short video clip that performs a specific, planned motion, without the model redrawing or drifting away from the source image.
Pick A: “How to Animate a Photo into a Video: My First Look at Google Veo 3 in Gemini
Direct tool match (Veo 3 in Gemini), animates real still photos with prompts shown on screen, and explicitly includes the failures alongside the successes, close to the before/after contrast the lesson wants.
Pick B: “Google Veo 3 Image To Video Tutorial For Beginners (Step by Step)
Shorter backup covering the same image-to-video motion-prompt workflow if Pick A runs long for class use.
Objective
Why This Matters
Text-to-video asks a model to solve two problems at once: what does this look like, and how does it move. Image-to-video lets you solve the first problem separately, in Module 1, and hand the model only the second one. That’s a meaningfully easier problem for the model, and it’s the direct route to keeping a Lesson 3 character consistent once it needs to move. If Lesson 3 was “lock the look,” this lesson is “keep the lock while adding motion.”
The Technique
The anchor image already carries subject, style, composition, and identity. Your prompt’s only job now is motion: a camera move (push in, pull out, pan, orbit) and/or a subject action (turns head, blinks, walks forward), and at any one time you should be directing only one of each, not stacking multiple camera moves or multiple subject actions.
The single most common trap here is re-describing the image’s appearance inside the motion prompt. If your motion prompt says “a woman with red hair in a blue jacket turns her head,” you’ve reintroduced appearance language that competes with the anchor image, and the model may reinterpret the face or costume to match your words instead of holding the anchor. Say only what changes: “camera pushes in slowly as the subject turns her head to look directly at camera.” The anchor already knows what she looks like.
Check the very first frame of the output against your anchor before you judge anything else. If frame one has already drifted, identity was lost at the starting gate and no amount of good motion afterward saves the shot.
Watch For This
Good
- Frame one is visually identical to your anchor image, no redraw.
- The motion performed matches exactly what you asked for (a specific camera move and/or a specific subject action).
Classic Failure
- The model reinterprets or redraws the subject at frame one because the motion prompt re-described appearance and competed with the anchor image.
- The clip performs a generic default motion (slow zoom, gentle pan) instead of the specific motion you asked for, because the prompt was too vague about what should move.
- Two camera moves or two subject actions were stacked in one prompt and the result blends them into something muddy, echoing Lesson 4’s single-action rule.
Your Drill
Take an anchor image from your Lesson 3 set (or generate one new anchor). Write a motion-only prompt naming one camera move and/or one subject action. Generate, check frame one against the anchor, adjust if needed, in 3 or fewer attempts. Submit the anchor image, the resulting clip, and the motion prompt used.
Done? Paste what you made into the AI coach below for notes against this lesson's pass checklist.
Pass Checklist
Lesson complete
Criterion met: The clip’s first frame matches the anchor image with no visible identity, costume, or color drift, and it performs the specific motion named in the prompt, not a generic default drift or zoom.
Next: Lesson 6: Cutting Together a 3-Shot AI SequenceHow solid did that feel?
Noted.
Coach Note
Read your motion prompt back before generating and cross out any adjective describing what the subject looks like. If there’s nothing left to cross out, you wrote it right. If you crossed out three phrases, that’s exactly where the identity drift is about to come from.
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