Depth-aware Neural Style Transfer
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Date
2017
Journal Title
Journal ISSN
Volume Title
Publisher
Association for Computing Machinery, Inc (ACM)
Abstract
Neural style transfer has recently received signi cant a ention and demonstrated amazing results. An e cient solution proposed by Johnson et al. trains feed-forward convolutional neural networks by de ning and optimizing perceptual loss functions. Such methods are typically based on high-level features extracted from pre-trained neural networks, where the loss functions contain two components: style loss and content loss. However, such pre-trained networks are originally designed for object recognition, and hence the high-level features o en focus on the primary target and neglect other details. As a result, when input images contain multiple objects potentially at di erent depths, the resulting images are o en unsatisfactory because image layout is destroyed and the boundary between the foreground and background as well as di erent objects becomes obscured. We observe that the depth map e ectively re ects the spatial distribution in an image and preserving the depth map of the content image a er stylization helps produce an image that preserves its semantic content. In this paper, we introduce a novel approach for neural style transfer that integrates depth preservation as additional loss, preserving overall image layout while performing style transfer.
Description
@inproceedings{10.1145:3092919.3092924,
booktitle = {Non-Photorealistic Animation and Rendering},
editor = {Holger Winnemoeller and Lyn Bartram},
title = {{Depth-aware Neural Style Transfer}},
author = {Liu, Xiao-Chang and Cheng, Ming-Ming and Lai, Yu-Kun and Rosin, Paul L.},
year = {2017},
publisher = {Association for Computing Machinery, Inc (ACM)},
ISSN = {-},
ISBN = {978-1-4503-5081-5},
DOI = {10.1145/3092919.3092924}
}