# AutoStageMix: Fully Automated Stage Cross-Editing System Utilizing Facial Features
## 2 Related Works  
### Cross-Editing Video Generation  
The generation of StageMix has been the subject of various studies. [PERSON] et al. [1] proposed a novel approach that uses the extraction of facial or body keypoints from multiple performance videos to automatically generate cross-edited footage. To prevent the inclusion of different scenes around transition points where similar faces appear, groups of five consecutive frames were compared to each other. However, this approach does not compare a sufficient number of frames to fully ensure visual immersion. Moreover, in their pose-based editing method, the researchers used the center coordinates of performers' bounding boxes to assess pose similarity within frames for cross-editing. However, this approach may lack precision.  
[PERSON] et al. proposed a novel method for generating StageMix using eye landmark features [2]. Their approach used dynamic programming to identify the optimal editing path and improved the visual continuity between frames by silhouette alignment and virtual camera view optimization. However, their study has certain limitations, including an insufficient explanation of the preprocessing steps, such as audio synchronization, and a reliance on commercial software for shot boundary detection. Furthermore, relying solely on eye landmarks to determine facial similarity could disrupt viewer immersion, particularly when face-based cross-editing involves different individuals or faces of different genders.  
[PERSON] et al. proposed a method for creating a mashup video by combining footage captured from multiple cameras filming a concert [3]. They synchronized the videos using audio fingerprints and analyzed both audio and video features to identify the most suitable segments for combination using a greedy algorithm. This method can be applied to create StageMix by combining multiple videos.  
Recently, [PERSON] et al. introduced Real Time GAZED, a real-time video editing system that stabilizes camera trajectories and assembles aesthetically pleasing shots on-the-fly [4]. While the system emphasizes dynamic camera control over face-based transitions, it shares AutoStageMix's goal of automating professional-quality editing and delivering an engaging viewing experience in real time. In addition, [PERSON] et al. proposed EditIQ, an automated editing framework that generates cinematically coherent videos from static, wide-angle footage [5]. The system simulates multiple virtual camera feeds and employs large language model (LLM)-based dialogue analysis along with visual saliency prediction to guide shot selection and transitions. EditIQ's approach to automated editing based on contextual and visual cues is relevant to broader research in cross-editing.  
In summary, previous studies have explored various approaches to automatically identifying transition points and assembling edited videos from multiple sources. These include face-based or pose-based alignment for performance videos, dialogue and saliency-driven editing for cinematic footage, and real-time systems for dynamic content creation. Building upon these findings of previous studies, our research aims to propose a fully automated StageMix generation system that focuses on improving transition accuracy through facial feature matching, while enhancing usability and viewer engagement.