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.
Neural texture compression (NTC) stores material textures compactly, but the dominant feature-pyramid design receives no filtering from the GPU texture unit: it learns each material at a fixed UV and level of detail and has no explicit anisotropic-filtering (AF) mechanism. Setting the filter from the footprint aspect ratio alone is not enough: a single input cannot carry both the shape of the footprint and its size, so the image is too blurry near the camera by the same amount at every distance. Our method, AF-NTC, instead conditions the decoder on the same three bounded values a hardware sampler uses: minor-axis scale (LOD), aspect ratio, and major-axis orientation. These drive a tap line along the major axis and a level blend across the minor axis, and we train against anisotropically filtered ground truth. At $25\%$ of a full BC7 mip chain, AF-NTC beats the no-AF trilinear-BC7 baseline on all ten material--scene pairs we compare ($+1.3$ to $+7.6$\,dB), and it is $0.0$ to $3.4$\,dB below the $4\times$-larger anisotropic-BC7, depending on the material.