  There currently exist two main approaches to reproducing visual appearance
using Machine Learning (ML): The first is training models that generalize over
different instances of a problem, e.g., different images of a dataset. As
one-shot approaches, these offer fast inference, but often fall short in
quality. The second approach does not train models that generalize across
tasks, but rather over-fit a single instance of a problem, e.g., a flash image
of a material. These methods offer high quality, but take long to train. We
suggest to combine both techniques end-to-end using meta-learning: We over-fit
onto a single problem instance in an inner loop, while also learning how to do
so efficiently in an outer-loop across many exemplars. To this end, we derive
the required formalism that allows applying meta-learning to a wide range of
visual appearance reproduction problems: textures, BRDFs, svBRDFs, illumination
or the entire light transport of a scene. The effects of meta-learning
parameters on several different aspects of visual appearance are analyzed in
our framework, and specific guidance for different tasks is provided.
Metappearance enables visual quality that is similar to over-fit approaches in
only a fraction of their runtime while keeping the adaptivity of general
models.
