Dual Attention Graph Convolutional Neural Network to Support Mocap Data Animation
Artykuł w czasopiśmie
MNiSW
100
Lista 2023
| Status: | |
| Autorzy: | Skublewska-Paszkowska Maria, Powroźnik Paweł, Barszcz Marcin, Dziedzic Krzysztof |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2023 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 5 |
| Wolumen/Tom: | 17 |
| Strony: | 313 - 325 |
| Impact Factor: | 1,0 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus | BazTech |
| Efekt badań statutowych | NIE |
| Finansowanie: | The research program titled “Biomechanical parameters of athletes in individual exercises” and based on the analysis of 3D motion data and EMG, realized in the Laboratory of Motion Anal-ysis and Interface Ergonomics was approved by the Commission for Research Ethics, No. 2/2016 dated 8.04.2016. |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Abstrakty: | angielski |
| The analysis of movements is one of the notable applications within the field of computer animation. Sophisticated motion capture techniques allow to acquire motion and store it in a digital form for further analysis. The combina- tion of these two aspects of computer vision enables the presentation of data in an accessible way for the user. The primary objective of this study is to introduce an artificial intelligence-based system for animating tennis motion capture data. The Dual Attention Graph Convolutional Network was applied. Its unique approach consists of two attention modules, one for body analysis and the other for tennis racket alignment. The input to the classifier is a sequence of three dimensional data generated from the Mocap system and containing an object of a player holding a tennis racket and presenting fundamental tennis hits, which are classified with great success, reaching a maximum accuracy over 95%. The recognised movements are further processed using dedicated software. Movement se- quences are assigned to the tennis player’s 3D digital model. In this way, realistic character animations are obtained, reflecting the recognised moves that can be further applied in movies, video games and other visual projects. |
