(a) pebble sculpture | (b) dish | (c) spaghetti |
Given a small input exemplar (left within each image), our method synthesizes a corresponding output with user specified coarse-scale output domain (right within each image).
A variety of phenomena can be characterized by repetitive small scale elements within
a large scale domain. Examples include a stack of fresh produce, a plate of spaghetti,
or a mosaic pattern. Although certain results can be produced via manual placement
or procedural/physical simulation, these methods can be labor intensive, difficult
to control, or limited to specific phenomena.
We present discrete element textures, a data-driven method for synthesizing repetitive
elements according to a small input exemplar and a large output domain. Our method
preserves both individual element properties and their aggregate distributions.
It is also general and applicable to a variety of phenomena, including different
dimensionalities, different element properties and distributions, and different
effects including both artistic and physically realistic ones. We represent each
element by one or multiple samples whose positions encode relevant element attributes
including position, size, shape, and orientation. We propose a sample-based neighborhood
similarity metric and an energy optimization solver to synthesize desired outputs
that observe not only input exemplars and output domains but also optional constraints
such as physics, orientation fields, and boundary conditions. As a further benefit,
our method can also be applied for editing existing element distributions.
Discrete element, texture, analysis, synthesis, sampling, editing, data driven