Implicit neural representation of textures

2026-02-02

AI Summary

This work addresses the longstanding trade-off among memory efficiency, rendering speed, and image quality in traditional discrete texture representations. It presents the first systematic application of implicit neural representations (INRs) to continuous texture modeling, introducing a novel neural network architecture that achieves high-fidelity texture synthesis directly in UV coordinate space with low memory footprint and efficient inference. By transcending the discrete limitations of conventional textures, the proposed method enables new applications such as mipmap fitting and spatially coherent INR-based texture generation. Extensive experiments demonstrate that the approach significantly reduces both memory consumption and inference latency while preserving visual fidelity, thereby validating the practicality and potential of INRs for texture representation and downstream graphics tasks.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Representation Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Implicit neural representation (INR) has proven to be accurate and efficient in various domains. In this work, we explore how different neural networks can be designed as a new texture INR, which operates in a continuous manner rather than a discrete one over the input UV coordinate space. Through thorough experiments, we demonstrate that these INRs perform well in terms of image quality, with considerable memory usage and rendering inference time. We analyze the balance between these objectives. In addition, we investigate various related applications in real-time rendering and down-stream tasks, e.g. mipmap fitting and INR-space generation.

Problem

• Implicit Neural Representation
• Texture Representation
• Continuous UV Space
• Real-time Rendering
• Memory Efficiency

Innovation

• Implicit Neural Representation
• Texture Modeling
• Continuous UV Mapping
• Real-time Rendering
• Mipmap Fitting