# AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features
## 5 Conclusions and Discussion  
In this work, we propose AutoStageMix, an automated system that generates cross-edited videos from stage performance footage without requiring specialized editing skills. By leveraging deep learning, the entire process is automated--from extracting facial features to identifying transition points and generating the StageMix. Notably, transition points identified using head pose estimation were more effective at selecting scenes with morphologically similar faces compared to those identified using facial landmarks, thereby improving transition quality. However, our work has several limitations. First, it does not synchronize transitions with the beat of the music, which can result in transitions occurring outside of musical highlights or at unnatural moments. This may make certain scenes feel awkward and reduce viewer immersion. To address this, future development should incorporate music-aware editing that aligns transitions with the flow of the music. Specifically, beat-detection algorithms like spectral flux onset detection or deep-learning-based tempo tracking could be integrated to identify rhythmically significant movement. These enhancements are expected to improve perceptual continuity and increase viewer satisfaction. Second, inconsistencies in body movements during FBT can disrupt viewer immersion. This highlights the need for more advanced motion analysis and matching techniques. Incorporating body landmarks into the editing process could help produce smoother and more natural transitions. As FBT are the most commonly used editing technique in StageMix, we prioritized facial alignment to ensure practical applicability. Incorporating pose similarity in combination with facial features remains a promising direction for future research. Third, footage taken under varying lighting conditions and camera angles may result in noticeable differences in color and brightness. Professional editors often retouch StageMix to reduce these inconsistencies while maintaining focus on the performers. We initially experimented with automatic color correction using histogram matching but found it difficult to apply effectively due to the dynamic nature of K-pop stage videos, where lighting and background colors can vary significantly within a single video. This aspect could be explored in future work to further improve visual coherence. Fourth, our method focuses on K-pop videos, particularly broadcast-stage performances commonly used in StageMix editing. This focus reflects the fact that StageMix originated from K-pop fan culture and is rarely applied outside of that context. While we did not evaluate other genres or lighting conditions, we expect AutoStageMix to operate in such settings as long as performer faces are visible. Lastly, the transition costs were manually set to reflect practical editing priorities in StageMix. While the system showed stability as long as the cost order was preserved, future work may explore more systematic parameter tuning to assess sensitivity and future optimize path quality. We aim to address these limitations and further develop the system to create StageMix content that delivers greater viewer satisfaction. Conceptualization, M.O. ; Data curation, M.O. and H.J. ; Formal analysis, M.O. and H.J. ; Investigation, M.O. and H.J. ; Methodology, M.O. and H.J. ; Project administration, D.L. ; Resources, M.O. and H.J. ; Software, M.O. and H.J. ; Supervision, D.L. ; Validation, M.O. and H.J. ; Writing--original draft, M.O. and H.J. ; Writing--review and editing, M.O., H.J. and D.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) under the metaverse support program, grant funded by the Korea government (MSIT) (IITP-RS-2024(2025)-00425383).