  We present color sails, a discrete-continuous color gamut representation that
extends the color gradient analogy to three dimensions and allows interactive
control of the color blending behavior. Our representation models a wide
variety of color distributions in a compact manner, and lends itself to
applications such as color exploration for graphic design, illustration and
similar fields. We propose a Neural Network that can fit a color sail to any
image. Then, the user can adjust color sail parameters to change the base
colors, their blending behavior and the number of colors, exploring a wide
range of options for the original design. In addition, we propose a Deep
Learning model that learns to automatically segment an image into
color-compatible alpha masks, each equipped with its own color sail. This
allows targeted color exploration by either editing their corresponding color
sails or using standard software packages. Our model is trained on a custom
diverse dataset of art and design. We provide both quantitative evaluations,
and a user study, demonstrating the effectiveness of color sail interaction.
Interactive demos are available at www.colorsails.com.
