Fixing Distorted Perspective on Curved Labels in Nano Banana

When generating images with Nano Banana, users often encounter a specific visual artifact where product labels appear distorted. This issue typically manifests as text that is unnaturally stretched, squashed, or floating away from the surface of a curved bottle or jar. Instead of wrapping smoothly around the cylinder, the typography may look like it has been painted onto a flat plane that was then forcibly bent, resulting in an unrealistic appearance. This distortion breaks the immersion of the image and can make the generated product look unprofessional or clearly artificial.

Understanding the Symptom and Known Facts

The primary symptom of this issue is a mismatch between the geometric shape of the container and the rendering of the label. You might see a cylindrical bottle where the text on the front appears normal, but the text on the sides stretches infinitely or disappears entirely. In some cases, the label seems to detach from the curve, hovering slightly above the surface rather than adhering to it. It is crucial to distinguish these visual glitches from actual product features. Nano Banana is an AI image generation and editing tool designed to create visuals based on text descriptions; it is not a physical skincare brand, nor does it produce physical bottles or jars. The software generates pixels based on patterns learned from data, meaning it does not inherently understand the physics of light reflection on curved surfaces unless explicitly guided.

While the tool supports both text-to-image and image-to-image workflows, prompt instructions describe desired outcomes without guaranteeing identity, label, object, or typography preservation. Therefore, when the AI attempts to wrap text around a complex 3D form, it may default to a 2D representation if the prompt does not sufficiently emphasize the three-dimensional nature of the scene. This is not a bug in the traditional sense but a limitation of how generative models interpret spatial relationships when the input description is ambiguous.

Separating Plausible Causes from Technical Limitations

To resolve the issue, one must separate plausible user errors from the inherent limitations of the current model. A common misconception is that the AI automatically knows how to render realistic shadows and perspective on every object type. However, the system relies heavily on the specificity of the prompt. If a user simply requests "a bottle with a label," the AI may generate a generic cylinder with a flat rectangle pasted on top, ignoring the curvature required for a realistic label wrap.

Another factor is the complexity of the geometry described. Curved labels require the AI to simulate the foreshortening effect, where parts of the label further from the viewer appear smaller and more compressed. If the prompt lacks directional cues about the camera angle or the lighting source, the AI struggles to calculate the correct perspective transformation. It is important to note that while the prompt library offers example prompts that users can copy, these examples are illustrative and do not guarantee perfect results for every specific request. Users should treat these examples as starting points for experimentation rather than absolute solutions.

Diagnosing and Fixing the Issue Through Prompt Refinement

Diagnosing the problem usually involves reviewing the initial prompt for missing geometric descriptors. To fix distorted perspective, you must explicitly define the object's shape and the label's behavior within the text input. Instead of vague terms, use precise language such as "cylindrical bottle," "wrapping label," or "text following the curvature." Describing the lighting and shadow can also help ground the object in a 3D space, making the label adhere more naturally to the surface.

For instance, rather than asking for "a soda bottle," try "a glass soda bottle with a realistic paper label wrapped tightly around the curved surface, text conforming to the cylinder's perspective." By adding constraints that force the AI to consider the volume of the object, you guide the generation process toward a more accurate representation. If using image-to-image mode, ensure the reference image clearly shows the curvature you wish to replicate. The goal is to provide enough context for the model to infer the necessary perspective shifts without overloading the prompt with contradictory instructions.

Verifying Your Results

After adjusting your prompts, verify the output by checking the alignment of the text relative to the bottle's edges. Does the text disappear or stretch excessively at the sides? Is there a consistent gradient of shadow that suggests the label is wrapping around? If the result still looks flat, refine the prompt further by specifying the camera angle, such as "eye-level shot" or "slightly angled view," which can sometimes help the AI better visualize the depth. Remember that while these adjustments significantly improve the likelihood of a correct outcome, they do not guarantee perfection in every single generation due to the probabilistic nature of the technology.

By treating the prompt as a set of geometric instructions rather than just a creative description, you can effectively minimize perspective distortions. For those ready to experiment with these refined techniques, Try Nano Banana to apply these strategies directly in the generator. Consistent practice with specific geometric vocabulary will yield the most reliable results for curved label scenarios.