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

TL;DR

V-Trans4Style is a transformer-based algorithm that recommends visually seamless transitions for videos, enabling style adaptation across various production types with improved accuracy demonstrated on a new dataset.

Contribution

The paper introduces a novel transformer-based model with style conditioning for adaptive video transition recommendation, outperforming existing methods and providing a new dataset for style-specific evaluation.

Findings

Outperforms state-of-the-art by 10-80% in Recall@K and mean rank

Style conditioning improves style similarity by around 12%

Introduces AutoTransition++ dataset with 6k videos categorized by style

Abstract

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.