MiraGe: Editable 2.5D Image Representations with Flat-Controlled 3D Gaussians
Abstract Implicit Neural Representations (INRs) encode images as continuous functions that map pixel coordinates to RGB values, achieving compact storage and high visual fidelity. Recent work such as GaussianImage replaces neural MLPs with collections of 2D Gaussian primitives, reaching similar reconstruction quality and compression but offering limited editability. In practice, creators often need to adjust content—move objects, bend a photo, cast new shadows, or create parallax—all of which are awkward within purely 2D or purely additive Gaussian schemes.
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