# Temporal and Contextual Transformer for Multi-Camera Editing of TV Shows

arXiv: https://arxiv.org/abs/2210.08737 · HTML: https://mlsanity.com/paper/2210.08737/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 40 references that are in the map.
- First posted on arXiv 17 Oct 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [MovieNet: A Holistic Dataset for Movie Understanding](https://mlsanity.com/paper/2007.10937/) (2020)
- [Towards An End-to-End Framework for Flow-Guided Video Inpainting](https://mlsanity.com/paper/2204.02663/) (2022)
- [Text-based Editing of Talking-head Video](https://mlsanity.com/paper/1906.01524/) (2019)
- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [A Unified Framework for Shot Type Classification Based on Subject Centric Lens](https://mlsanity.com/paper/2008.03548/) (2020)
- [Learning Trailer Moments in Full-Length Movies](https://mlsanity.com/paper/2008.08502/) (2020)
- [MovieCuts: A New Dataset and Benchmark for Cut Type Recognition](https://mlsanity.com/paper/2109.05569/) (2021)
- [The Anatomy of Video Editing: A Dataset and Benchmark Suite for AI-Assisted Video Editing](https://mlsanity.com/paper/2207.09812/) (2022)
- [Learning to Cut by Watching Movies](https://mlsanity.com/paper/2108.04294/) (2021)
- [B-Script: Transcript-based B-roll Video Editing with Recommendations](https://mlsanity.com/paper/1902.11216/) (2019)
- [Transcript to Video: Efficient Clip Sequencing from Texts](https://mlsanity.com/paper/2107.11851/) (2021)
- [AutoTransition: Learning to Recommend Video Transition Effects](https://mlsanity.com/paper/2207.13479/) (2022)
- [Automatic Non-Linear Video Editing Transfer](https://mlsanity.com/paper/2105.06988/) (2021)
- [A New Dimension in Testimony: Relighting Video with Reflectance Field Exemplars](https://mlsanity.com/paper/2104.02773/) (2021)

## Built on by (most cited first, 8 of 8)

- [Shot-by-Shot: Film-Grammar-Aware Training-Free Audio Description Generation](https://mlsanity.com/paper/2504.01020/) (2025)
- [Generative Timelines for Instructed Visual Assembly](https://mlsanity.com/paper/2411.12293/) (2024)
- [Reframe Anything: LLM Agent for Open World Video Reframing](https://mlsanity.com/paper/2403.06070/) (2024)
- [Real Time GAZED: Online Shot Selection and Editing of Virtual Cameras from Wide-Angle Monocular Video Recordings](https://mlsanity.com/paper/2311.15581/) (2023)
- [Pseudo Dataset Generation for Out-of-Domain Multi-Camera View Recommendation](https://mlsanity.com/paper/2410.13585/) (2024)
- [V-Trans4Style: Visual Transition Recommendation for Video Production Style Adaptation](https://mlsanity.com/paper/2501.07983/) (2025)
- [When and How to Cut Classical Concerts? A Multimodal Automated Video Editing Approach](https://mlsanity.com/paper/2510.05661/) (2025)
- [A Dual-Transformer for Multi-Camera View Recommendation](https://mlsanity.com/paper/2608.25601/) (2026)

## Abstract (arXiv)

The ability to choose an appropriate camera view among multiple cameras plays a vital role in TV shows delivery. But it is hard to figure out the statistical pattern and apply intelligent processing due to the lack of high-quality training data. To solve this issue, we first collect a novel benchmark on this setting with four diverse scenarios including concerts, sports games, gala shows, and contests, where each scenario contains 6 synchronized tracks recorded by different cameras. It contains 88-hour raw videos that contribute to the 14-hour edited videos. Based on this benchmark, we further propose a new approach temporal and contextual transformer that utilizes clues from historical shots and other views to make shot transition decisions and predict which view to be used. Extensive experiments show that our method outperforms existing methods on the proposed multi-camera editing benchmark.