Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1111/cgf.13831 | COMPUTER GRAPHICS FORUM |
Field | DocType | Volume |
Computer vision,Computer graphics (images),Computer science,Artificial intelligence | Journal | 38.0 |
Issue | ISSN | Citations |
7.0 | 0167-7055 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ri yu | 1 | 0 | 2.03 |
| Hwangpil Park | 2 | 19 | 3.08 |
| Jehee Lee | 3 | 1912 | 118.33 |