We present a method to transfer the appearance of one or a few exemplar
SVBRDFs to a target image representing similar materials. Our solution is
extremely simple: we fine-tune a deep appearance-capture network on the
provided exemplars, such that it learns to extract similar SVBRDF values from
the target image. We introduce two novel material capture and design workflows
that demonstrate the strength of this simple approach. Our first workflow
allows to produce plausible SVBRDFs of large-scale objects from only a few
pictures. Specifically, users only need take a single picture of a large
surface and a few close-up flash pictures of some of its details. We use
existing methods to extract SVBRDF parameters from the close-ups, and our
method to transfer these parameters to the entire surface, enabling the
lightweight capture of surfaces several meters wide such as murals, floors and
furniture. In our second workflow, we provide a powerful way for users to
create large SVBRDFs from internet pictures by transferring the appearance of
existing, pre-designed SVBRDFs. By selecting different exemplars, users can
control the materials assigned to the target image, greatly enhancing the
creative possibilities offered by deep appearance capture.

本文提出了一种将基于样例的表面视角双向反射分布函数转移到类似材料的目标图像中的简单方法，并介绍了两种工作流程，一种允许用户从单个大表面的照片中捕捉大规模对象的表面，另一种可以从网络图片中创建大型 SVBRDF。

面向大规模物质转移的引导微调

Guided Fine-Tuning for Large-Scale Material Transfer

Empowered by deep learning, recent methods for material capture can estimate
a spatially-varying reflectance from a single photograph. Such lightweight
capture is in stark contrast with the tens or hundreds of pictures required by
traditional optimization-based approaches. However, a single image is often
simply not enough to observe the rich appearance of real-world materials. We
present a deep-learning method capable of estimating material appearance from a
variable number of uncalibrated and unordered pictures captured with a handheld
camera and flash. Thanks to an order-independent fusing layer, this
architecture extracts the most useful information from each picture, while
benefiting from strong priors learned from data. The method can handle both
view and light direction variation without calibration. We show how our method
improves its prediction with the number of input pictures, and reaches high
quality reconstructions with as little as 1 to 10 images -- a sweet spot
between existing single-image and complex multi-image approaches.

该论文提出了一种深度学习方法，可以从不确定数量的拍摄的无序图片中估计材料外观，无需校准，并通过提取每张图片中的最有用的信息并从数据中学到的先验知识，处理视角和光线方向的变化，从而实现对材料的捕捉，并在单张至多张图片的输入条件下，实现与现有单张和复杂多张方法之间的完美平衡。