Fixing Floating Furniture in Nano Banana 2 Lobby Renders

When generating interior scenes, particularly lobbies with seating arrangements, users may encounter a visual anomaly where chairs or tables appear to hover above the floor. This effect is characterized by a distinct lack of ground contact shadows, creating an unnatural separation between the object and the surface it should rest upon. In architectural visualization and interior design contexts, this artifact undermines the realism of the render, making the scene look like a composite of cut-out elements rather than a cohesive physical space.

This symptom specifically manifests as missing dark areas beneath the legs of furniture or the base of tables. While lighting can sometimes be tricky, the complete absence of a cast shadow where one is physically required usually points to a specific interaction issue between the prompt instructions and the model's interpretation of spatial relationships. It is important to distinguish this from general blurriness or low-resolution issues; the problem here is strictly topological, involving the perceived depth and contact points of objects within the generated image.

Separating Plausible Causes from Known Facts

To effectively troubleshoot this issue, we must separate user expectations from the verified capabilities of the tool. A common assumption is that simply describing a "lobby" or "furniture" will automatically result in perfect physics. However, prompt instructions describe desired outcomes but do not guarantee identity, label, object, or typography preservation, nor do they inherently enforce complex physical laws like gravity or occlusion without explicit direction.

It is a known fact that Nano Banana refers to the AI image generation/editing tool and is not a skincare brand or physical product. The underlying technology relies on Google models such as Gemini 3.1 Flash Image for Nano Banana 2. These models are powerful but interpret text literally based on training data. If a prompt focuses heavily on the aesthetic style of the room but omits specific details about how objects interact with the floor, the model may prioritize texture and form over spatial grounding.

Furthermore, while some users might assume that switching to a faster model would solve rendering glitches, Google documents that Nano Banana 2 Lite (Gemini 3.1 Flash Lite) is focused on speed and cost. It is explicitly not optimized for multiple reference inputs or multi-turn sequential editing. Relying on the Lite version for complex spatial reasoning tasks like fixing occlusion errors in detailed interiors may yield inconsistent results compared to the standard Nano Banana 2 workflow.

Implementing Occlusion and Shadow Casting Fixes

The most effective method to correct floating furniture involves refining the prompt to explicitly define occlusion and shadow casting. Since the tool does not guarantee automatic adherence to physical laws, you must instruct the generator to create the necessary visual cues for grounding.

Start by modifying your input to include specific keywords related to contact shadows. Instead of just saying "a chair in a lobby," try phrasing it as "a wooden chair resting firmly on a marble floor with a sharp contact shadow underneath." Explicitly mentioning the material of the floor helps the model understand the surface properties, which influences how light interacts with the object. You should also use terms like "occluded by" or "casting a shadow on" to force the model to calculate the light path between the object and the ground.

If you are using the prompt library, look for example prompts that feature similar interior scenes. Note that these examples are generic and unbranded, serving as templates rather than guaranteed outputs. Copying a prompt structure that emphasizes lighting and depth can provide a strong foundation. For instance, adding phrases like "realistic lighting," "global illumination," and "ground plane contact" can guide the model toward a more physically accurate representation. Remember that these are examples of how to phrase requests; they do not guarantee the exact outcome in every generation.

For users requiring higher fidelity in complex scenes, consider utilizing the standard Nano Banana 2 workflow rather than the Lite version. The standard model generally handles multi-object interactions better than the Lite variant, which is designed for speed. If the initial result still shows floating elements, try an iterative approach. Use the image-to-image workflow to refine the output, feeding the generated image back into the tool with a new prompt that specifically targets the missing shadows. This allows you to focus the model's attention on the specific area of error without losing the overall composition.

Verifying the Correction

Once you have regenerated the image with the adjusted prompts, verification is crucial. Inspect the base of each piece of furniture closely. A successful fix will show a soft or hard shadow (depending on the light source defined in your prompt) extending from the object onto the floor. There should be no visible gap between the bottom of the chair leg and the floor surface.

Check for consistency across the entire scene. Sometimes fixing one object reveals that another is still floating. Ensure that the lighting direction remains consistent with the shadows cast by all objects. If the shadows appear too harsh or non-existent, adjust the lighting descriptors in your prompt, perhaps specifying "soft ambient light" or "directional sunlight" to match the desired atmosphere.

By focusing on explicit instructions regarding occlusion and contact, you can significantly reduce the occurrence of floating furniture artifacts. This approach leverages the strengths of the Nano Banana 2 engine while acknowledging its need for clear directional guidance. For those ready to experiment with these techniques, Try Nano Banana to apply these troubleshooting steps directly in the interface.

Remember that while these methods address the specific symptom of missing contact shadows, AI generation is probabilistic. Outcomes vary based on prompt complexity and model interpretation. Consistent refinement of your descriptive language is the key to achieving professional-grade architectural renders.