Keyframe extraction is a widely applied remedy for issues faced with 3D motion capture -based computer animation. In this paper, we propose a novel keyframe extraction method, where the motion is represented in linear rotation invariant coordinates and the dimensions covering 95% of the data are automatically selected using principal component analysis. Then, by K-means classification, the summarized data is clustered and a keyframe is extracted from each cluster based on cosine similarity. To validate the method, an online user study was conducted. The results of the user study show that 45% of the participants preferred the keyframes extracted using the proposed method, outperforming the alternative by 6%.
| Primary Language | English |
|---|---|
| Subjects | Mathematical Sciences |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | October 20, 2022 |
| Submission Date | July 25, 2022 |
| Acceptance Date | September 5, 2022 |
| Published in Issue | Year 2022 Volume: 26 Issue: 5 |
INDEXING & ABSTRACTING & ARCHIVING
Bu eser Creative Commons Atıf-Ticari Olmayan 4.0 Uluslararası Lisans kapsamında lisanslanmıştır .