NPAR2017

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https://diglib.eg.org/handle/10.2312/2631863

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Browsing NPAR2017 by Subject "Computational photography"

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    Depth-aware Neural Style Transfer
    (Association for Computing Machinery, Inc (ACM), 2017) Liu, Xiao-Chang; Cheng, Ming-Ming; Lai, Yu-Kun; Rosin, Paul L.; Holger Winnemoeller and Lyn Bartram
    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.
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    Mixed Illumination Analysis in Single Image for Interactive Color Grading
    (Association for Computing Machinery, Inc (ACM), 2017) DuchĂȘne, Sylvain; Aliaga, Carlos; Pouli, Tania; PĂ©rez, Patrick; Holger Winnemoeller and Lyn Bartram
    Colorists often use keying or rotoscoping tools to access and edit particular colors or parts of the scene. Although necessary, this is a time-consuming and potentially imprecise process, as it is not possible to fully separate the influence of light sources in the scene from the colors of objects and actors within it. To simplify this process, we present a new solution for automatically estimating the color and influence of multiple illuminants, based on image variation analysis. Using this information, we present a new color grading tool for simply and interactively editing the colors of de- tected illuminants, which fits naturally in color grading workflows. We demonstrate the use of our solution in several scenes, evaluating the quality of our results by means of a psychophysical study.