Browsing by Author "Eom, Haegwang"

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    Real-time Content Projection onto a Tunnel from a Moving Subway Train
    (The Eurographics Association, 2021) Kim, Jaedong; Eom, Haegwang; Kim, Jihwan; Kim, Younghui; Noh, Junyong; Lee, Sung-Hee and Zollmann, Stefanie and Okabe, Makoto and Wünsche, Burkhard
    In this study, we present the first actual working system that can project content onto a tunnel wall from a moving subway train so that passengers can enjoy the display of digital content through a train window. To effectively estimate the position of the train in a tunnel, we propose counting sleepers, which are installed at regular interval along the railway, using a distance sensor. The tunnel profile is constructed using pointclouds captured by a depth camera installed next to the projector. The tunnel profile is used to identify projectable sections that will not contain too much interference by possible occluders. The tunnel profile is also used to retrieve the depth at a specific location so that a properly warped content can be projected for viewing by passengers through the window when the train is moving at runtime. Here, we show that the proposed system can operate on an actual train.
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    Synthesizing Character Animation with Smoothly Decomposed Motion Layers
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Eom, Haegwang; Choi, Byungkuk; Cho, Kyungmin; Jung, Sunjin; Hong, Seokpyo; Noh, Junyong; Benes, Bedrich and Hauser, Helwig
    The processing of captured motion is an essential task for undertaking the synthesis of high‐quality character animation. The motion decomposition techniques investigated in prior work extract meaningful motion primitives that help to facilitate this process. Carefully selected motion primitives can play a major role in various motion‐synthesis tasks, such as interpolation, blending, warping, editing or the generation of new motions. Unfortunately, for a complex character motion, finding generic motion primitives by decomposition is an intractable problem due to the compound nature of the behaviours of such characters. Additionally, decomposed motion primitives tend to be too limited for the chosen model to cover a broad range of motion‐synthesis tasks. To address these challenges, we propose a generative motion decomposition framework in which the decomposed motion primitives are applicable to a wide range of motion‐synthesis tasks. Technically, the input motion is smoothly decomposed into three motion layers. These are base‐level motion, a layer with controllable motion displacements and a layer with high‐frequency residuals. The final motion can easily be synthesized simply by changing a single user parameter that is linked to the layer of controllable motion displacements or by imposing suitable temporal correspondences to the decomposition framework. Our experiments show that this decomposition provides a great deal of flexibility in several motion synthesis scenarios: denoising, style modulation, upsampling and time warping.