Backdrop: Enabling Any-Reference 3D Generation with One Line of Code

We propose Backdrop, a one-line-of-code intervention that enables controllable 3D generation from arbitrary image references. Existing image-conditioned 3D generators provide a convenient interface for reference-based control, but this interface implicitly assumes object-centric images. When the reference is instead a texture, scene, painting, or material photograph, existing stylization methods can severely distort or fragment the generated geometry. We trace this failure to a mismatch in conditioning structure: object-centric references contain a large population of background patch tokens that absorb substantial cross-attention in deeper generator blocks, while full-frame references do not. Removing these tokens from object references reproduces the failure, while restoring them to non-object references repairs it. Backdrop exploits this observation by appending a cached set of patch tokens from a single blank image to the reference condition. This restores the generator's expected fallback population without changing the reference, retraining the model, optimizing per instance, or altering the underlying control method. Across diverse textures, scenes and artistic images, and with different 3D stylization methods, consistently improves geometric integrity while preserving strong reference transfer and content shape, making arbitrary images reliable controls for 3D generation. Results and code are available at https://backdrop-3d.pages.dev/.