[Submitted on 5 Nov 2026 (
[v1](https://arxiv.org/abs/2611.07289v1)), last revised 10 Nov 2026 (this version, v2)]
[View PDF](https://arxiv.org/pdf/2611.07289)
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Abstract:We propose a lightweight neural texture upscaling method. A single generalized MLP is trained on a diverse collection of texture patches and integrated into the texture sampling stage, upscaling low-resolution textures on-the-fly at rendering time. Because the model is generalized rather than overfit to specific content, no per-asset training is required - the same model applies to any existing scene without modifying texture data or the asset pipeline. Storing lower-resolution base textures reduces disk footprint, upscaling at sampling time eliminates the need to store high-resolution textures in VRAM, and sharing a single set of model weights across all textures ensures non-divergent GPU execution. We evaluate our method across multiple texture classes, demonstrating consistent improvement over analytic upscaling methods in perceptual quality.
Submission history #
From: Takahiro Harada [
[view email](https://arxiv.org/show-email/06a8bc53/2611.07289)]
**Thu, 5 Nov 2026 01:11:19 UTC (9,837 KB)**
[\[v1\]](https://arxiv.org/abs/2611.07289v1)
**[v2]** Tue, 10 Nov 2026 20:12:34 UTC (9,849 KB)
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