Fixing Inconsistent Perspective in Hotel Lobby Floor Plans with Nano Banana 2

When generating architectural visualizations for hotel lobbies, users often encounter a specific symptom: inconsistent perspective where the floor tiles appear to warp unnaturally or the walls do not meet the ceiling at logical angles. This issue typically manifests as a misalignment between vertical structures and horizontal surfaces, making the space look physically impossible or visually jarring. The primary goal is to achieve a coherent one-point or two-point perspective that accurately reflects the spatial layout of the lobby.

It is important to separate plausible causes from known facts regarding this behavior. While some users might suspect that the AI model is hallucinating structural elements due to low-resolution inputs, the core issue often lies in the ambiguity of the prompt instructions rather than a fundamental failure of the image generation engine. Known facts indicate that Nano Banana 2 supports text-to-image workflows where prompt instructions describe desired outcomes. However, these instructions do not guarantee identity, label, object, or typography preservation, nor do they inherently enforce strict geometric rules without explicit guidance. Therefore, the distortion is frequently a result of insufficiently defined camera parameters within the text description.

Understanding Camera Angle Descriptions

The most effective method to correct perspective errors involves refining how the camera angle is described in the prompt. When an image shows a warped floor plan, it suggests the model interpreted the scene from a floating or undefined viewpoint rather than a fixed, grounded perspective. To address this, users should explicitly define the camera position relative to the floor plane. For instance, instead of simply requesting a "hotel lobby view," the prompt should specify a "low-angle shot looking down the hallway" or a "straight-on eye-level view of the reception desk."

By anchoring the camera description to specific architectural features, such as the floor tiles or the base of the columns, the model receives clearer constraints on how to render the depth of the room. This approach helps align the vanishing points, ensuring that parallel lines on the floor converge logically toward a single point in the distance. It is crucial to remember that prompt instructions are descriptive; they guide the output but do not guarantee perfect geometric precision in every iteration. Users may need to iterate on their phrasing to find the optimal balance between artistic style and structural accuracy.

Distinguishing Model Capabilities and Limitations

While troubleshooting perspective issues, it is essential to understand the specific capabilities of the tool being used. Google documents Nano Banana 2 as Gemini 3.1 Flash Image, which is designed for general-purpose image generation. In contrast, Nano Banana Pro corresponds to Gemini 3 Pro Image, and Nano Banana 2 Lite maps to Gemini 3.1 Flash Lite Image. These are distinct models with different optimizations.

A common pitfall is assuming that all versions handle complex architectural geometry equally. Google describes Nano Banana 2 Lite as focused on speed and cost. Crucially, it is not optimized for multiple reference inputs or multi-turn sequential editing. If a user attempts to fix a persistent perspective error by uploading multiple reference images of the same lobby or engaging in a long chain of edits to refine the floor alignment, using the Lite version may yield suboptimal results or fail to maintain consistency. For complex tasks requiring precise control over architectural details like floor plans, the standard Nano Banana 2 or Pro models are generally more suitable. Always verify that you are using the appropriate model for the complexity of the task before concluding that the prompt itself is flawed.

Verifying Fixes and Iterating on Prompts

After adjusting the camera angle descriptions, verification is the final step to ensure the perspective is corrected. Generate a new image with the revised prompt and inspect the alignment of the floor tiles against the walls. If the distortion persists, try varying the specific terminology used to describe the viewpoint, such as switching from "wide angle" to "telephoto lens" or specifying the height of the camera in feet or meters.

Users can also explore the prompt library available on the website to see how other professionals phrase similar requests. These example prompts offer a starting point for understanding how to articulate complex spatial relationships. Remember that these examples are untested in your specific context and serve only as inspiration. Do not expect them to produce identical results without modification. If the issue remains unresolved after several iterations, consider simplifying the scene description to reduce competing visual cues that might confuse the model's perspective calculation.

For those ready to apply these techniques immediately, Try Nano Banana to experiment with your own hotel lobby prompts and observe how small changes in camera description impact the final output. By focusing on clear, grounded camera instructions and selecting the right model for the job, users can significantly reduce perspective inconsistencies and create professional-quality architectural visualizations.

Sources: Google Gemini image generation documentation (https://ai.google.dev/gemini-api/docs/image-generation)