Measured Bidirectional Texture Function (BTF) can faithfully reproduce a realistic appearance but is costly to acquire and store due to its 6D nature (2D spatial and 4D angular). Therefore, it is practical and necessary for rendering to synthesize BTFs from a small example patch. While previous methods managed to produce plausible results, we find that they seldomly take into consideration the property of being dynamic, so a BTF must be synthesized before the rendering process, resulting in limited size, costly pre-generation and storage issues. In this paper, we propose a dynamic BTF synthesis scheme, where a BTF at any position only needs to be synthesized when being queried. Our insight is that, with the recent advances in neural dimension reduction methods, a BTF can be decomposed into disjoint low-dimensional components. We can perform dynamic synthesis only on the positional dimensions, and during rendering, recover the BTF by querying and combining these low-dimensional functions with the help of a lightweight Multilayer Perceptron (MLP). Consequently, we obtain a fully dynamic 6D BTF synthesis scheme that does not require any pre-generation, which enables efficient rendering of our infinitely large and non-repetitive BTFs on the fly. We demonstrate the effectiveness of our method through various types of BTFs taken from UBO2014.

翻译：实测双向纹理函数（BTF）能够真实再现材质外观，但由于其六维特性（二维空间与四维角度），获取与存储成本高昂。因此，在渲染中通过小尺寸示例块合成BTF具有现实必要性。现有方法虽能生成视觉可信的结果，但大多未考虑动态合成特性，导致BTF必须在渲染前预生成，进而引发尺寸受限、预计算成本高及存储负担等问题。本文提出一种动态BTF合成方案，其中任意位置的BTF仅在查询时实时合成。我们的核心思路是：借助神经降维方法的最新进展，可将BTF解耦为独立的低维分量。通过仅对空间维度进行动态合成，并在渲染时借助轻量级多层感知机（MLP）查询并组合这些低维函数，即可重构完整BTF。由此，我们实现了无需预生成的全动态六维BTF合成方案，能够实时高效渲染无限尺度且无重复模式的BTF。基于UBO2014数据集的多类BTF实验验证了本方法的有效性。