Realistic scene appearance modeling has advanced rapidly with Gaussian Splatting, which enables real-time, high-quality rendering. Recent advances introduced per-primitive textures that incorporate spatial color variations within each Gaussian, improving their expressiveness. However, texture-based Gaussians parameterize appearance with a uniform per-Gaussian sampling grid, allocating equal sampling density regardless of local visual complexity, which leads to inefficient texture space utilization. We introduce FACT-GS, a Frequency-Aligned Complexity-aware Texture Gaussian Splatting framework that allocates texture sampling density according to local visual frequency. Grounded in adaptive sampling theory, FACT-GS reformulates texture parameterization as a differentiable sampling-density allocation problem, replacing the uniform textures with a learnable frequency-aware allocation strategy implemented via a deformation field whose Jacobian modulates local sampling density. Built on 2D Gaussian Splatting, FACT-GS performs non-uniform sampling on fixed-resolution texture grids, preserving real-time performance while recovering sharper high-frequency details under the same parameter budget.

翻译：基于高斯泼溅的场景真实外观建模取得了快速发展，实现了实时高质量渲染。近期研究通过引入逐基元纹理，将空间颜色变化融入每个高斯体内，提升了其表现力。然而，基于纹理的高斯体采用均匀的逐高斯采样网格参数化外观，无论局部视觉复杂度如何都分配相同的采样密度，导致纹理空间利用率低下。本文提出FACT-GS——一种频率对齐的复杂度感知纹理高斯泼溅框架，该框架根据局部视觉频率分配纹理采样密度。基于自适应采样理论，FACT-GS将纹理参数化重新表述为可微分的采样密度分配问题，提出通过形变场替代均匀纹理，其雅可比矩阵可调节局部采样密度，实现可学习的频率感知分配策略。在二维高斯泼溅基础上，FACT-GS对固定分辨率纹理网格执行非均匀采样，在保持实时性能的同时，于相同参数预算下重建更清晰的高频细节。