  Given an outfit, what small changes would most improve its fashionability?
This question presents an intriguing new vision challenge. We introduce
Fashion++, an approach that proposes minimal adjustments to a full-body
clothing outfit that will have maximal impact on its fashionability. Our model
consists of a deep image generation neural network that learns to synthesize
clothing conditioned on learned per-garment encodings. The latent encodings are
explicitly factorized according to shape and texture, thereby allowing direct
edits for both fit/presentation and color/patterns/material, respectively. We
show how to bootstrap Web photos to automatically train a fashionability model,
and develop an activation maximization-style approach to transform the input
image into its more fashionable self. The edits suggested range from swapping
in a new garment to tweaking its color, how it is worn (e.g., rolling up
sleeves), or its fit (e.g., making pants baggier). Experiments demonstrate that
Fashion++ provides successful edits, both according to automated metrics and
human opinion. Project page is at
http://vision.cs.utexas.edu/projects/FashionPlus.
