Fixing Nano Banana 2: Inconsistent Object Occlusion in Layered Furniture Scenes

When generating complex interior scenes with Nano Banana 2, users may encounter a specific visual artifact known as inconsistent object occlusion. This symptom manifests when furniture items appear to float above the floor rather than resting on it, or when distinct pieces of furniture visually merge into a single, indistinct mass. These issues are particularly prevalent in daylight compositions where lighting gradients and shadows play a critical role in defining spatial depth. The result is an image that lacks structural integrity, making the room feel physically impossible despite the high quality of the rendering.

It is important to distinguish between plausible causes and verified facts regarding this behavior. While some users might suspect that the lighting engine itself is flawed or that the daylight simulation is too aggressive, the core issue often lies in how the model interprets layering instructions within the prompt. The AI may struggle to prioritize which objects should be in front of others when multiple large items are described in close proximity. This is not necessarily a failure of the underlying Google Gemini models, but rather a challenge in translating complex spatial relationships into a two-dimensional output without explicit guidance.

Separating Plausible Causes from Verified Model Behaviors

To effectively troubleshoot this issue, one must separate user expectations from the documented capabilities of the system. A common misconception is that the tool automatically understands physical laws of gravity and collision for every object mentioned. However, prompt instructions describe desired outcomes; they do not guarantee identity, label, object, or typography preservation. When describing a scene with a sofa, a coffee table, and a bookshelf all in one frame, the model relies heavily on the order and specificity of the text to determine occlusion logic.

Furthermore, while the website supports text-to-image and image-to-image workflows, the specific version of the model being used dictates the complexity of the scene it can handle. Google documents Nano Banana 2 as Gemini 3.1 Flash Image (gemini-3.1-flash-image), Nano Banana Pro as Gemini 3 Pro Image (gemini-3-pro-image), and Nano Banana 2 Lite as Gemini 3.1 Flash Lite Image (gemini-3.1-flash-lite-image). These are distinct Google image models with different optimization goals. For instance, Google describes Nano Banana 2 Lite as focused on speed and cost. It is not optimized for multiple reference inputs or multi-turn sequential editing. If you are attempting to fix occlusion errors by uploading multiple reference images of furniture layers, using the Lite version may exacerbate the problem because it lacks the necessary context retention for such complex tasks.

It is also crucial to note that the website has a Nano Banana Pro page at /nanobananapro and a page named Nano Banana Lite at /nanobananalite. However, these pages do not by themselves establish support for Google Nano Banana 2 Lite features if those features are not explicitly detailed. Google model names and capabilities must not be presented as proof of availability or identical features on this website. Therefore, assuming that the Lite version handles complex occlusion as well as the Pro version is a risk that can lead to further inconsistencies.

Step-by-Step Diagnosis and Prompt Engineering Fixes

The most effective way to resolve floating or merging furniture is through precise prompt engineering. Since the AI does not inherently know which object sits on top of another, you must explicitly define the spatial hierarchy. Instead of listing items randomly, structure your prompt to describe the scene from the foreground to the background. Start by naming the closest object, followed by the middle ground, and finally the background elements. This technique helps the model assign correct depth values during generation.

For example, rather than saying "a living room with a sofa, table, and lamp," try "a wooden coffee table in the immediate foreground, behind it a plush sofa, and in the far background a tall bookshelf near a window." By anchoring the description to specific positions relative to the viewer, you reduce the ambiguity that leads to occlusion errors. Additionally, ensure that the lighting description reinforces the depth. Mentioning "long shadows cast by the furniture" can help the model understand that objects are resting on a surface rather than hovering.

If you are working with existing images, consider using the image-to-image workflow to refine the composition. However, be aware that the prompt library offers example prompts that users can copy or take into the generator, but these examples are generic and unbranded. They serve as starting points but may need modification to address specific occlusion issues in your unique scene. Always label untested prompt examples as examples when adapting them to your needs.

Verification and Final Checks

After adjusting your prompt, verify the results by checking the interaction between the furniture pieces. Do the legs of the chair touch the floor? Does the back of the sofa disappear behind the armrest of the chair? If the objects still appear to merge, try simplifying the scene by reducing the number of furniture items in a single generation pass. Complex scenes with many overlapping objects increase the cognitive load on the model, raising the probability of occlusion failures.

If the issue persists across multiple attempts with different prompts, it may be beneficial to switch models. Nano Banana 2 (Gemini 3.1 Flash Image) generally offers a balance of speed and detail, while Nano Banana Pro (Gemini 3 Pro Image) may provide better handling of complex spatial reasoning due to its enhanced capabilities. Avoid relying on Nano Banana 2 Lite for these specific tasks unless you have simplified the scene significantly, as its focus on speed and cost comes at the expense of handling multiple reference inputs or intricate layering.

By carefully structuring your descriptions and selecting the appropriate model for the complexity of your interior daylight scene, you can significantly reduce instances of inconsistent object occlusion. Remember that while the tool is powerful, it requires clear, logical instructions to replicate real-world physics accurately. Try Nano Banana to experiment with these refined prompting strategies and see how they improve your generated interiors.