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Image1 Identity Lock Morning Brief

A reusable identity template: inherit only the face and hairstyle from Image1, ignore everything else, and play out a groggy morning bathroom routine.

Aug 19, 202685 views

Prompt

Seedance 2.5

Subject: Person from Image1. Face and hairstyle must match Image1 exactly; do not change identity. Ignore its outfit, background, pose, and text. Outfit: oversized casual sleepwear, messy morning look. 9:16 vertical. Old iPhone everyday-video aesthetic throughout: static phone propped on bathroom sink, standard 1x lens, no distortion, heavy noise/compression, slight mirror steam, warm/off-white overhead lighting. Natural skin, pores, flyaways, under-eye tiredness. No beauty filter, grading, cinematic/CG look, fisheye, vignette, or legible text. 0–4s: Groggy, walks in yawning, glances at phone/mirror, rubs eyes. 4–8s: Brushes messy hair and mutters, “okay... five more minutes wasn't enough.” 8–12s: Steps on bathroom scale, looks down, gives a subtle eye-roll/sigh/shrug, steps off. 12–16s: Opens small closet, holds up two shirts one after another, indecisive. 16–19s: Chooses the first, tosses the other aside, looks at camera and says softly, “…this one. good enough for today,” with a tired smile; freeze final frame. Bathroom clutter: toothpaste/skincare bottles, but all labels illegible. Sound only: bathroom ambience, hairbrush, scale beep/click, fabric rustle, close muffled sleepy voice with slight bathroom reverb. Keep all cuts and actions; no skipped scenes or on-screen text.

How to Adapt This Prompt for Seedance 2.5

  • 1Swap the subject while keeping the camera language: the shot progression, timing beats (e.g. 0-2s / 2-5s) and lighting notes are what make the motion feel cinematic.
  • 2Keep the duration and aspect ratio in mind when editing — longer shots need more keyframes of action, vertical formats favor tighter framing and faster pacing.
  • 3Preserve the negative-style constraints (no text overlays, no face changes, no logos) — they prevent the most common artifacts in AI video generation.

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