Browsing by Author "Yu, Ri"

Now showing 1 - 3 of 3
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    Figure Skating Simulation from Video
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Yu, Ri; Park, Hwangpil; Lee, Jehee; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    Figure skating is one of the most popular ice sports at the Winter Olympic Games. The skaters perform several skating skills to express the beauty of the art on ice. Skating involves moving on ice while wearing skate shoes with thin blades; thus, it requires much practice to skate without losing balance. Moreover, figure skating presents dynamic moves, such as jumping, artistically. Therefore, demonstrating figure skating skills is even more difficult to achieve than basic skating, and professional skaters often fall during Winter Olympic performances. We propose a system to demonstrate figure skating motions with a physically simulated human-like character. We simulate skating motions with non-holonomic constraints, which make the skate blade glide on the ice surface. It is difficult to obtain reference motions from figure skaters because figure skating motions are very fast and dynamic. Instead of using motion capture data, we use key poses extracted from videos on YouTube and complete reference motions using trajectory optimization. We demonstrate figure skating skills, such as crossover, three-turn, and even jump. Finally, we use deep reinforcement learning to generate a robust controller for figure skating skills.
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    Multi‐Segment Foot for Human Modelling and Simulation
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Park, Hwangpil; Yu, Ri; Lee, Jehee; Benes, Bedrich and Hauser, Helwig
    Realistic modelling of a human‐like character is one of the main topics in computer graphics to simulate human motion physically and also look realistically. Of the body parts, a human foot interacts with the ground, and plays an essential role in weight transmission, balancing posture and assisting ambulation. However, in the previous researches, the foot model was often simplified into one or two rigid bodies connected by a revolute joint. We propose a new foot model consisting of multiple segments to reproduce human foot shape and its functionality accurately. Based on the new model, we develop a foot pose controller that can reproduce foot postures that are generally not obtained in motion capture data. We demonstrate the validity of our foot model and the effectiveness of our foot controller with a variety of foot motions in a physics‐based simulation.
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    Reconstructing Baseball Pitching Motions from Video
    (The Eurographics Association, 2023) Kim, Jiwon; Kim, Dongkwon; Yu, Ri; Chaine, Raphaëlle; Deng, Zhigang; Kim, Min H.
    Baseball is one of the most loved sports in the world. In baseball game, the pitcher's control ability is a key factor for determining the outcome of the game. There are a lot of video data shooting baseball games, and learning baseball pitching motions from video can be possible thanks to the pose estimation techniques. However, reconstructing pitching motions using pose estimators is challenging. When we watch a baseball game, motion blur occurs inevitably because the pitcher throws a ball into the strike zone as fast as possible. To tackle this problem, We propose a framework using physics simulation and deep reinforcement learning to reconstruct baseball pitching motions based on unsatisfactory poses estimated from video. We set the target point and design rewards to encourage the character to throw the ball to the target point. Consequently, we can reconstruct plausible pitching motion.