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

TL;DR

V-Trans4Style addresses the challenge of adapting videos to different production styles by automatically recommending sequences of visual transitions. It introduces a transformer-based encoder–decoder to generate temporally coherent transition sequences from input clips, complemented by a style conditioning module that uses activation maximization to steer outputs toward a target style. The AutoTransition++ dataset, with 6k videos and five production-style labels, underpins the evaluation and demonstrates improvements over a state-of-the-art baseline in Recall@K and mean rank, while the SCM increases style-similarity by approximately 12% on average. Collectively, the approach provides a practical foundation for style-aware video editing and highlights future directions for integrating other editing elements and ethical considerations in automated production styling.

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.

Figures (13)

• Figure 1: Our goal is to recommend the optimal visual transition sequence for enabling the adaptation of a given video to any desired production style. We propose V-Trans4Style, a novel bottom-up approach consisting of an encoder-decoder architecture and a style conditioning module.
• Figure 1: (A) shows the transition distribution in AutoTransition++ dataset. (B) shows the distribution of video production style labels available.
• Figure 2: Bivariate distribution observed between the different styles and the visual transitions deployed across the 1379 video production style annotated videos within AutoTransition++.
• Figure 2: $\mathcal{D}_\psi$ is used as a model capable of reconstructing the encoder feature vector $h^{tfe}$. This property is used in the development of the reconstruction loss mentioned in Sec. \ref{['sec:appr3-module2']}.
• Figure 3: V-Trans4Style: Ordered clips $\{c_1, c_2,..,c_n\}$ in $V$ is fed to an Encoder $\mathcal{E}$ to obtain $z_t$. Decoder $\mathcal{D}$ takes in $z_t$ and outputs a sequence of transitions in $n-1$ steps. At each step $t$, the $\mathcal{D}$ outputs $tr_t$. The masked transformed decoder uses past transition embeddings in every step to compute $h_t^{tfd}$. $z_t$ is same (i.e., $z = z_t$) across all steps during the joint training of $\mathcal{E}$ and $\mathcal{D}$. Only components connected by $\rightarrow$ are active during training. $\mathcal{SCM}$ is an inference-time module. At inference, for every step $t$, $\mathcal{SCM}$ takes in current $z_t$ to estimate the appropriate $z_{t+1}$ for the next step to produce production style favorable transition $tr_t$.