GaINeR: Geometry-Aware Implicit Network Representation
This addresses the need for more interactive and physically aware image manipulation tools in computer graphics and vision, though it appears incremental as it builds on existing INR methods.
The paper tackles the problem of traditional Implicit Neural Representations lacking explicit geometric structure and local editing capabilities for 2D images, proposing GaINeR, which combines Gaussian distributions with neural networks to enable continuous representation, interpretable geometry, and flexible local editing.
Implicit Neural Representations (INRs) have become an essential tool for modeling continuous 2D images, enabling high-fidelity reconstruction, super-resolution, and compression. Popular architectures such as SIREN, WIRE, and FINER demonstrate the potential of INR for capturing fine-grained image details. However, traditional INRs often lack explicit geometric structure and have limited capabilities for local editing or integration with physical simulation, restricting their applicability in dynamic or interactive settings. To address these limitations, we propose GaINeR: Geometry-Aware Implicit Network Representation, a novel framework for 2D images that combines trainable Gaussian distributions with a neural network-based INR. For a given image coordinate, the model retrieves the K nearest Gaussians, aggregates distance-weighted embeddings, and predicts the RGB value via a neural network. This design enables continuous image representation, interpretable geometric structure, and flexible local editing, providing a foundation for physically aware and interactive image manipulation. The official implementation of our method is publicly available at https://github.com/WJakubowska/GaINeR.