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Researchers in computer vision are developing a method for Action Quality Assessment (AQA) that evaluates the quality of human actions in videos rather than identifying them. Specifically for figure skating, the task involves estimating the final scores from a video of a short program. It serves as an auxiliary assessment for judging skaters’ performances. Despite the significance of accurately predicting individual jump scores due to their substantial impact on final scores, prior studies have overlooked this aspect. Although videos concentrate on a solitary skater, they often include extraneous elements unrelated to assessing the quality. Consequently, expert humans discard non-essential data to make visually precise evaluations. Our research has illuminated the gaze patterns of judges and skaters when assessing jumps, developing a jump-performance prediction model that leverages their gaze patterns to filter out irrelevant information. In addition, we enhanced its predictive precision by incorporating kinematic data from a tracking system. The findings revealed a marked contrast in gaze patterns: skaters focused mainly on the face, while judges paid more attention to the lower body. Integrating these gaze patterns into our model improved its learning efficiency, with the model improved accuracy by assimilating the gaze data from both groups of specialists. Our work marks an innovative step towards merging human insight and artificial intelligence to tackle the challenge of jump performance evaluation in figure skating, offering valuable contributions to computer vision and sports science.

顔を見ない日はないというくらい、「顔」は私達にとってあたり前の存在ですが、私達は一体どれほど「顔」のことを知っているのでしょうか。そのような「顔」を総合的に研究するのが「顔学」です。 顔学には、動物学や人類学をはじめ、解剖学、生理学、歯学、心理学、社会学の文化的な対象として扱われるだけでなく、演劇や美術などの芸術学、コンピュータの分野では、情報学、さらに、美容学、人相学など、実に多様な学問分野と関係しています。 本書では、私達と切り離すことのできない「顔」の、歴史的・文化的・社会的・科学的側面を中項目の事典としてまとめられていることにより、多様な分野を横断する知識にも容易にアクセスが可能になっています。 日本顔学会創立20周年記念出版として、「顔学」について体系化を行った、初めての百科事典です。