AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features
StageMix is a video compilation of multiple stage performances of the same song, edited seamlessly together using appropriate editing points. However, generating a StageMix requires specialized editing techniques and is a considerable time-consuming process. To address this challenge, we introduce A...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/13/7613 |
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| author | Minjun Oh Howon Jang Daeho Lee |
| author_facet | Minjun Oh Howon Jang Daeho Lee |
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| collection | DOAJ |
| description | StageMix is a video compilation of multiple stage performances of the same song, edited seamlessly together using appropriate editing points. However, generating a StageMix requires specialized editing techniques and is a considerable time-consuming process. To address this challenge, we introduce AutoStageMix, an automated StageMix generation system designed to perform all processes automatically. The system is structured into five principal stages: preprocessing, feature extraction, identifying a transition point, editing path determination, and StageMix generation. The initial stage of the process involves audio analysis to synchronize the sequences across all input videos, followed by frame extraction. After that, the facial features are extracted from each video frame. Next, transition points are identified, which form the basis for face-based transitions, inter-stage cuts, and intra-stage cuts. Subsequently, a cost function is defined to facilitate the creation of cross-edited sequences. The optimal editing path is computed using Dijkstra’s algorithm to minimize the total cost of editing. Finally, the StageMix is generated by applying appropriate editing effects tailored to each transition type, aiming to maximize visual appeal. Experimental results suggest that our method generally achieves lower NME scores than existing StageMix generation approaches across multiple test songs. In a user study with 21 participants, AutoStageMix achieved viewer satisfaction comparable to that of professionally edited StageMixes, with no statistically significant difference between the two. AutoStageMix enables users to produce StageMixes effortlessly and efficiently by eliminating the need for manual editing. |
| format | Article |
| id | doaj-art-3f2928d3768e4c7f8d8bba8092fd3dca |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-3f2928d3768e4c7f8d8bba8092fd3dca2025-08-20T03:16:41ZengMDPI AGApplied Sciences2076-34172025-07-011513761310.3390/app15137613AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial FeaturesMinjun Oh0Howon Jang1Daeho Lee2Department of Software Convergence, Kyung Hee University, Yongin 17104, Republic of KoreaDepartment of Software Convergence, Kyung Hee University, Yongin 17104, Republic of KoreaDepartment of Software Convergence, Kyung Hee University, Yongin 17104, Republic of KoreaStageMix is a video compilation of multiple stage performances of the same song, edited seamlessly together using appropriate editing points. However, generating a StageMix requires specialized editing techniques and is a considerable time-consuming process. To address this challenge, we introduce AutoStageMix, an automated StageMix generation system designed to perform all processes automatically. The system is structured into five principal stages: preprocessing, feature extraction, identifying a transition point, editing path determination, and StageMix generation. The initial stage of the process involves audio analysis to synchronize the sequences across all input videos, followed by frame extraction. After that, the facial features are extracted from each video frame. Next, transition points are identified, which form the basis for face-based transitions, inter-stage cuts, and intra-stage cuts. Subsequently, a cost function is defined to facilitate the creation of cross-edited sequences. The optimal editing path is computed using Dijkstra’s algorithm to minimize the total cost of editing. Finally, the StageMix is generated by applying appropriate editing effects tailored to each transition type, aiming to maximize visual appeal. Experimental results suggest that our method generally achieves lower NME scores than existing StageMix generation approaches across multiple test songs. In a user study with 21 participants, AutoStageMix achieved viewer satisfaction comparable to that of professionally edited StageMixes, with no statistically significant difference between the two. AutoStageMix enables users to produce StageMixes effortlessly and efficiently by eliminating the need for manual editing.https://www.mdpi.com/2076-3417/15/13/7613cross-video editingstage mixingvideo synthesis |
| spellingShingle | Minjun Oh Howon Jang Daeho Lee AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features Applied Sciences cross-video editing stage mixing video synthesis |
| title | AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features |
| title_full | AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features |
| title_fullStr | AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features |
| title_full_unstemmed | AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features |
| title_short | AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features |
| title_sort | autostagemix fully automated stage cross editing system utilizing facial features |
| topic | cross-video editing stage mixing video synthesis |
| url | https://www.mdpi.com/2076-3417/15/13/7613 |
| work_keys_str_mv | AT minjunoh autostagemixfullyautomatedstagecrosseditingsystemutilizingfacialfeatures AT howonjang autostagemixfullyautomatedstagecrosseditingsystemutilizingfacialfeatures AT daeholee autostagemixfullyautomatedstagecrosseditingsystemutilizingfacialfeatures |