Figure 1. Given an input photograph, the proposed algorithm automatically generates a set of images with stylized skies. The proposed
algorithm exploits visual semantics for sky editing, in which scene parsing is first performed on the input image, and such semantic information
is utilized for the subsequent steps including sky segmentation, search and replacement.
Abstract
Skies are common backgrounds in photos but are often less interesting
due to the time of photographing. Professional photographers
correct this by using sophisticated tools with painstaking efforts
that are beyond the command of ordinary users. In this work,
we propose an automatic background replacement algorithm that
can generate realistic, artifact-free images with a diverse styles of
skies. The key idea of our algorithm is to utilize visual semantics
to guide the entire process including sky segmentation, search and
replacement. First we train a deep convolutional neural network for
semantic scene parsing, which is used as visual prior to segment
sky regions in a coarse-to-fine manner. Second, in order to find
proper skies for replacement, we propose a data-driven sky search
scheme based on semantic layout of the input image. Finally, to
re-compose the stylized sky with the original foreground naturally,
an appearance transfer method is developed to match statistics locally
and semantically. We show that the proposed algorithm can
automatically generate a set of visually pleasing results. In addition,
we demonstrate the effectiveness of the proposed algorithm
with extensive user studies.
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BibTex
@article{Tsai_SIGGRAPH_2016,
author = {Y.-H. Tsai and X. Shen and Z. Lin and K. Sunkavalli and M.-H. Yang},
journal = {ACM Transactions on Graphics (Proc. SIGGRAPH)},
title = {Sky is Not the Limit: Semantic-Aware Sky Replacement}, volume = {35},
number = {4},
year = {2016}}
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