Nano Banana 2 Tutorial: Aligning Cast Shadows with Curved Floor Planes in 3D Renders

Understanding Shadow Behavior on Non-Planar Surfaces

Creating photorealistic 3D renders often involves complex geometry that challenges standard lighting assumptions. A common issue arises when objects are placed on curved floors, such as domes, arches, or spherical platforms. In many rendering engines or AI generations, shadows appear flat and detached from the surface curvature, breaking the illusion of physical reality. This tutorial focuses on using Nano Banana 2 to address this specific visual artifact.

Nano Banana refers to the AI image generation and editing tool discussed here. It is not a skincare brand, bottle, jar, or physical subject. The goal is to guide the model to understand that light interacts with the underlying geometry. When an object sits on a curved plane, its shadow must follow the contour of that plane, tapering and bending naturally rather than stretching out in a straight line. Achieving this requires precise prompt engineering that explicitly describes the interaction between the light source, the object, and the curved ground.

Step-by-Step Workflow for Curved Shadow Alignment

To achieve accurate shadow alignment, you need to structure your input carefully within the Nano Banana 2 interface. This tool supports text-to-image and image-to-image workflows, allowing you to start from scratch or refine existing renders. Follow these numbered steps to generate images where shadows adhere to curved surfaces:

1. Select the Correct Model: Navigate to the Nano Banana 2 product page at /nanobanana2. Ensure you are using the standard Nano Banana 2 model (Gemini 3.1 Flash Image) rather than Nano Banana 2 Lite. Google documents Nano Banana 2 Lite as focused on speed and cost. It is not optimized for multiple reference inputs or multi-turn sequential editing. For complex tasks like aligning shadows on curves, the standard model offers better reasoning capabilities regarding spatial relationships.
2. Prepare Your Base Image: If working in image-to-image mode, upload a render where the object and floor geometry are visible but the shadow is incorrect (flat). Ensure the floor clearly shows a spherical or arched profile.
3. Craft the Descriptive Prompt: Write a prompt that explicitly defines the lighting direction and the surface geometry. Avoid generic terms. Instead of saying "a shadow," specify "a soft shadow conforming to the convex curvature of the floor." Mention the light source angle relative to the curve.
4. Adjust Strength Parameters: If using image-to-image, set the denoising strength appropriately. Too low may retain the flat shadow; too high might alter the object itself. Aim for a balance that modifies only the shadow area while preserving the object's identity.
5. Iterate and Refine: Generate the image and review the result. If the shadow still appears disconnected, refine the prompt to emphasize the contact point between the object base and the curved surface.

Constructing Effective Prompts for Spatial Accuracy

The quality of the output depends heavily on how you describe the physics of the scene. Since prompt instructions describe desired outcomes and do not guarantee identity, label, object, or typography preservation, you must be descriptive about the geometry. Below is an example prompt structure you can adapt. Note that these are examples and results may vary based on the specific input image and model interpretation.

Example Prompt Structure: "A photorealistic 3D render of a [object description] resting on a [specific curved shape, e.g., hemispherical dome] floor. The light source is positioned at a [angle] degree angle. The cast shadow must strictly follow the curvature of the floor, bending smoothly around the sphere without appearing flat or floating. High contrast, sharp contact points, realistic lighting physics."

You can try generating variations by changing the specific geometric terms or light angles. Remember that Nano Banana names the image tool, never the depicted cosmetic brand or physical product. The focus remains entirely on the digital manipulation of light and shadow.

Evaluating Results and Troubleshooting Common Issues

After generating your image, you must judge whether the shadow alignment is successful. Look for continuity between the object's base and the shadow. Does the shadow darken immediately where the object touches the curve? Does it elongate and thin out as it follows the arc away from the object? If the shadow looks like a flat ellipse regardless of the floor shape, the prompt likely lacked sufficient emphasis on the surface topology.

If the results are unsatisfactory, consider the following fixes:

• Clarify Geometry: Explicitly name the curve type (e.g., "cylindrical arch," "spherical bowl") in the prompt.
• Increase Lighting Specificity: Define the sun or lamp position more precisely to force the shadow to cast in a specific direction relative to the curve.
• Model Selection: If you accidentally used Nano Banana 2 Lite, switch to the standard Nano Banana 2. As noted, the Lite version is not optimized for these complex spatial edits.
• Reference Input: In image-to-image mode, ensure the uploaded image clearly shows the curve so the model has a visual reference for the shadow path.

For further exploration of features and to access the generator, visit Try Nano Banana. This resource provides the necessary environment to apply these techniques. By focusing on the specific mechanics of light and surface interaction, you can significantly enhance the realism of your 3D compositions.

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. While this website has a Nano Banana 2 product page at /nanobanana2, always verify current capabilities directly on the platform. For more information on the underlying technology, refer to the Google Gemini image generation documentation available at https://ai.google.dev/gemini-api/docs/image-generation.