# V-Trans4Style: Visual Transition Recommendation for Video Production Style Adaptation

arXiv: https://arxiv.org/abs/2501.07983 · HTML: https://mlsanity.com/paper/2501.07983/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 33 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [FateZero: Fusing Attentions for Zero-shot Text-based Video Editing](https://mlsanity.com/paper/2303.09535/) (2023)
- [Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models](https://mlsanity.com/paper/2303.17599/) (2023)
- [CLIP-It! Language-Guided Video Summarization](https://mlsanity.com/paper/2107.00650/) (2021)
- [FLAVR: Flow-Agnostic Video Representations for Fast Frame Interpolation](https://mlsanity.com/paper/2012.08512/) (2020)
- [Condensed Movies: Story Based Retrieval with Contextual Embeddings](https://mlsanity.com/paper/2005.04208/) (2020)
- [MovieCuts: A New Dataset and Benchmark for Cut Type Recognition](https://mlsanity.com/paper/2109.05569/) (2021)
- [VDPVE: VQA Dataset for Perceptual Video Enhancement](https://mlsanity.com/paper/2303.09290/) (2023)
- [Splatting-based Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/2201.10075/) (2022)
- [Fast Video Shot Transition Localization with Deep Structured Models](https://mlsanity.com/paper/1808.04234/) (2018)
- [VIVE3D: Viewpoint-Independent Video Editing using 3D-Aware GANs](https://mlsanity.com/paper/2303.15893/) (2023)
- [Soundini: Sound-Guided Diffusion for Natural Video Editing](https://mlsanity.com/paper/2304.06818/) (2023)
- [AutoTransition: Learning to Recommend Video Transition Effects](https://mlsanity.com/paper/2207.13479/) (2022)
- [Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks](https://mlsanity.com/paper/1705.03281/) (2017)
- [Match Cutting: Finding Cuts with Smooth Visual Transitions](https://mlsanity.com/paper/2210.05766/) (2022)
- [Query-Conditioned Three-Player Adversarial Network for Video Summarization](https://mlsanity.com/paper/1807.06677/) (2018)
- [Temporal and Contextual Transformer for Multi-Camera Editing of TV Shows](https://mlsanity.com/paper/2210.08737/) (2022)

## Built on by (most cited first, 1 of 1)

- [Autoregressive Modeling of Film with Applications in Video Montage](https://mlsanity.com/paper/2607.14645/) (2026)

## Abstract (arXiv)

We introduce V-Trans4Style, an innovative algorithm tailored for dynamic video content editing needs. It is designed to adapt videos to different production styles like documentaries, dramas, feature films, or a specific YouTube channel's video-making technique. Our algorithm recommends optimal visual transitions to help achieve this flexibility using a more bottom-up approach. We first employ a transformer-based encoder-decoder network to learn recommending temporally consistent and visually seamless sequences of visual transitions using only the input videos. We then introduce a style conditioning module that leverages this model to iteratively adjust the visual transitions obtained from the decoder through activation maximization. We demonstrate the efficacy of our method through experiments conducted on our newly introduced AutoTransition++ dataset. It is a 6k video version of AutoTransition Dataset that additionally categorizes its videos into different production style categories. Our encoder-decoder model outperforms the state-of-the-art transition recommendation method, achieving improvements of 10% to 80% in Recall@K and mean rank values over baseline. Our style conditioning module results in visual transitions that improve the capture of the desired video production style characteristics by an average of around 12% in comparison to other methods when measured with similarity metrics. We hope that our work serves as a foundation for exploring and understanding video production styles further.