Retail Warehouse Multi-View 3x3 Reconstruction AI Image Prompt
Using the provided reference image, create a coherent multi-view scene reconstruction with the same person and retail warehouse environment. Generate a {argument name="aspect ratio" default="16:9"} image divided into an exact {argument name="grid layout" default="3x3"} contact sheet. In all 9 panels, keep the subject’s identity, clothing, sunglasses, display table, and store setting consistent, but redraw the surrounding scene from varied camera angles and distances so the person’s standing position and environment remain spatially plausible. Required 9 views: 1. Front-facing medium view from across the sunglasses table. 2. Rear view showing the subject’s back and the table ahead. 3. Right-side profile view beside the display. 4. Left-side profile view from closer to the table. 5. Three-quarter front view similar to the reference. 6. Three-quarter front/right view with shelves behind. 7. Rear-left view looking over the table. 8. Rear three-quarter view with the subject centered near the display. 9. Wide elevated view showing the full sunglasses table, subject, and surrounding warehouse aisles. Use {argument name="model or style note" default="GPT-image-2.5 Sunburst"} style photorealistic synthesis. Emphasize front, back, left, and right views as mandatory. Do not add captions, speech bubbles, borders beyond the clean white grid dividers, or any extra people.
Content: Formula: reference image + multi-view reconstruction; 3x3 contact sheet, 9 fixed camera angles; consistent identity, clothing, sunglasses, table, warehouse; 16:9; GPT-image-2.5 photoreal; no captions, bubbles, extra borders or people.
Pros: Precise camera list; strong consistency and spatial constraints.
Cons: Depends on reference image; 9-panel identity/scene drift likely; style/model binding is rigid.
Reference image: Clearly depends on a reference image.
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