GaINeR: Geometry-Aware Implicit Neural Representation for Image Editing

Implicit Neural Representations (INRs) are widely used formodeling continuous 2D images, enabling high-fidelity reconstruction,super-resolution, and compression. Architectures such as SIREN, WIRE,and FINER demonstrate their ability to capture fine image details. How-ever, conventional INRs lack explicit geometric structure, limiting localediting, and integration with physical simulation. To address these lim-itations, we propose GaINeR (Geometry-Aware Implicit Network Rep-resentation), a novel framework for 2D images that combines trainableGaussian distributions with a neural network-based INR. For a givenimage coordinate, the model retrieves the K nearest Gaussians, aggre-gates distance-weighted embeddings, and predicts the RGB value viaa neural network. This design enables continuous image representation,interpretable geometric structure, and flexible local editing, providing afoundation for physically aware and interactive image manipulation. Ourmethod supports geometry-consistent transformations, seamless super-resolution, and integration with physics-based simulations. Moreover, theGaussian representation allows lifting a single 2D image into a geometry-aware 3D representation, enabling depth-guided editing. Experimentsdemonstrate that GaINeR achieves state-of-the-art reconstruction qual-ity while maintaining flexible and physically consistent image editing.