Browsing by Author "Yamasaki, Toshihiko"

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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.