Browsing by Author "Price, Brian"

Now showing 1 - 3 of 3
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    Image Recoloring Based on Object Color Distributions
    (The Eurographics Association, 2019) Afifi, Mahmoud; Price, Brian; Cohen, Scott; Brown, Michael S.; Cignoni, Paolo and Miguel, Eder
    We present a method to perform automatic image recoloring based on the distribution of colors associated with objects present in an image. For example, when recoloring an image containing a sky object, our method incorporates the observation that objects of class 'sky' have a color distribution with three dominant modes for blue (daytime), yellow/red (dusk/dawn), and dark (nighttime). Our work leverages recent deep-learning methods that can perform reasonably accurate object-level segmentation. By using the images in datasets used to train deep-learning object segmentation methods, we are able to model the color distribution of each object class in the dataset. Given a new input image and its associated semantic segmentation (i.e., object mask), we perform color transfer to map the input image color histogram to a set of target color histograms that were constructed based on the learned color distribution of the objects in the image. We show that our framework is able to produce compelling color variations that are often more interesting and unique than results produced by existing methods.
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    Learning to Trace: Expressive Line Drawing Generation from Photographs
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Inoue, Naoto; Ito, Daichi; Xu, Ning; Yang, Jimei; Price, Brian; Yamasaki, Toshihiko; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    In this paper, we present a new computational method for automatically tracing high-resolution photographs to create expressive line drawings. We define expressive lines as those that convey important edges, shape contours, and large-scale texture lines that are necessary to accurately depict the overall structure of objects (similar to those found in technical drawings) while still being sparse and artistically pleasing. Given a photograph, our algorithm extracts expressive edges and creates a clean line drawing using a convolutional neural network (CNN). We employ an end-to-end trainable fully-convolutional CNN to learn the model in a data-driven manner. The model consists of two networks to cope with two sub-tasks; extracting coarse lines and refining them to be more clean and expressive. To build a model that is optimal for each domain, we construct two new datasets for face/body and manga background. The experimental results qualitatively and quantitatively demonstrate the effectiveness of our model. We further illustrate two practical applications.
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    RGB2AO: Ambient Occlusion Generation from RGB Images
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Inoue, Naoto; Ito, Daichi; Hold-Geoffroy, Yannick; Mai, Long; Price, Brian; Yamasaki, Toshihiko; Panozzo, Daniele and Assarsson, Ulf
    We present RGB2AO, a novel task to generate ambient occlusion (AO) from a single RGB image instead of screen space buffers such as depth and normal. RGB2AO produces a new image filter that creates a non-directional shading effect that darkens enclosed and sheltered areas. RGB2AO aims to enhance two 2D image editing applications: image composition and geometryaware contrast enhancement. We first collect a synthetic dataset consisting of pairs of RGB images and AO maps. Subsequently, we propose a model for RGB2AO by supervised learning of a convolutional neural network (CNN), considering 3D geometry of the input image. Experimental results quantitatively and qualitatively demonstrate the effectiveness of our model.