We formulate SVBRDF estimation from photographs as a diffusion task. To model
the distribution of spatially varying materials, we first train a novel
unconditional SVBRDF diffusion backbone model on a large set of 312,165
synthetic spatially varying material exemplars. This SVBRDF diffusion backbone
model, named MatFusion, can then serve as a basis for refining a conditional
diffusion model to estimate the material properties from a photograph under
controlled or uncontrolled lighting. Our backbone MatFusion model is trained
using only a loss on the reflectance properties, and therefore refinement can
be paired with more expensive rendering methods without the need for
backpropagation during training. Because the conditional SVBRDF diffusion
models are generative, we can synthesize multiple SVBRDF estimates from the
same input photograph from which the user can select the one that best matches
the users' expectation. We demonstrate the flexibility of our method by
refining different SVBRDF diffusion models conditioned on different types of
incident lighting, and show that for a single photograph under colocated flash
lighting our method achieves equal or better accuracy than existing SVBRDF
estimation methods.

我们将从照片中估计 SVBRDF 作为一项扩散任务，并使用一种新颖的无条件 SVBRDF 扩散基础模型 MatFusion 对分布具有空间变化的材料进行建模，然后通过条件扩散模型细化该基础模型以估计照片中的材料特性，并展示了我们的方法在不同类型的入射光照条件下对单张照片的精度能够达到或超过现有的 SVBRDF 估计方法。