# AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features
## 1 Introduction  
StageMix is a video that cross-edits multiple stage performances of the same song. It has gained significant popularity, particularly in the context of K-pop idol performances. The visual satisfaction and immersion of a StageMix largely depend on how natural the scene transitions are, as well as their frequency and timing. Therefore, creating a StageMix requires a skilled editor who aligns the input videos to a consistent sequence, identifies appropriate editing points, and utilizes techniques such as hard cut editing and face-based editing to ensure seamless transitions between different performance videos. Moreover, producing a single StageMix requires approximately 10 h of manual work by a professional editor. These constraints make creating cross-edited videos a challenging and time-consuming task. For fans who want to create their own StageMix, the task can be especially challenging.  
To address the challenges of manual editing, prior works have proposed automated approaches for StageMix generation. [PERSON] et al. [1] employed facial and body keypoints to guide transitions, but their method lacked robustness due to limited frame comparison and simplistic heuristics. [PERSON] et al. [2] improved visual continuity using eye landmarks and dynamic programming, yet their approach relied on commercial tools for shot boundary detection and provided a limited explanation of the preprocessing step.  
Building upon the limitations of previous works, we present AutoStageMix, a system designed to automatically generate StageMix from multiple stage performances to address these challenges. AutoStageMix introduces several key improvements over prior approaches. First, it incorporates head pose features to assess morphological similarity more comprehensively and enhance the accuracy of face-based transitions. Second, it removes dependency on external software by fully automating the entire process. The system consists of five key stages: preprocessing of the input video, feature extraction from the preprocessed data, identifying a transition point, determining an editing path, and generating the StageMix.  
The first stage involves analyzing the audio of the input videos to synchronize sequences and extract frames. The second stage focuses on extracting facial features from the extracted frames. The third stage involves identifying potential transition points based on facial features. In the fourth stage, an optimal editing path is determined with the goal of maximizing visual satisfaction and immersion, based on the identified transition points. The final stage generates the StageMix by applying suitable transition effects tailored to each transition type, based on the determined editing path. These five steps enable the creation of an optimal StageMix output from the input footage.  
We conducted both a quantitative evaluation and a user study to demonstrate the effectiveness of AutoStageMix. The quantitative evaluation assessed the accuracy of face matching, which is a critical aspect of face-based transitions. In a user study, participants were presented with StageMix created by AutoStageMix and scored their level of satisfaction. These evaluations confirmed the practicality and efficiency of AutoStageMix, thereby demonstrating its potential to greatly benefit both K-pop fans and general users.