# Visualizing Celebrity Dynamics in Video Content: A Proposed Approach Using Face Recognition Timestamp Data

arXiv: https://arxiv.org/abs/2510.03292 · HTML: https://mlsanity.com/paper/2510.03292/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 5 of 5)

- [CelebV-HQ: A Large-Scale Video Facial Attributes Dataset](https://mlsanity.com/paper/2207.12393/) (2022)
- [EmoCo: Visual Analysis of Emotion Coherence in Presentation Videos](https://mlsanity.com/paper/1907.12918/) (2019)
- [Motion Browser: Visualizing and Understanding Complex Upper Limb Movement Under Obstetrical Brachial Plexus Injuries](https://mlsanity.com/paper/1907.09146/) (2019)
- [Leading by the Nodes: A Survey of Film Industry Network Analysis and Datasets](https://mlsanity.com/paper/2408.06393/) (2024)
- [Analyzing Social Networks of Actors in Movies and TV Shows](https://mlsanity.com/paper/2411.00975/) (2024)

## Abstract (arXiv)

In an era dominated by video content, understanding its structure and dynamics has become increasingly important. This paper presents a hybrid framework that combines a distributed multi-GPU inference system with an interactive visualization platform for analyzing celebrity dynamics in video episodes. The inference framework efficiently processes large volumes of video data by leveraging optimized ONNX models, heterogeneous batch inference, and high-throughput parallelism, ensuring scalable generation of timestamped appearance records. These records are then transformed into a comprehensive suite of visualizations, including appearance frequency charts, duration analyses, pie charts, co-appearance matrices, network graphs, stacked area charts, seasonal comparisons, and heatmaps. Together, these visualizations provide multi-dimensional insights into video content, revealing patterns in celebrity prominence, screen-time distribution, temporal dynamics, co-appearance relationships, and intensity across episodes and seasons. The interactive nature of the system allows users to dynamically explore data, identify key moments, and uncover evolving relationships between individuals. By bridging distributed recognition with structured, visually-driven analytics, this work enables new possibilities for entertainment analytics, content creation strategies, and audience engagement studies.