Abstract
Implicit neural representations operate continuously over UV coordinate space, demonstrating good image quality while balancing memory usage and rendering time, with applications in real-time rendering and downstream tasks.
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.
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Implicit neural representation of textures
@misc
{KH2026INR-Tex,
title={Implicit neural representation of textures},
author={Albert Kwok and Zheyuan Hu and Dounia Hammou},
year={2026},
eprint={2602.02354},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2602.02354},
}
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