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

2025-01-14

European Conference on Computer Vision

AI Summary

To address unnatural transitions and imprecise style adaptation in video style transfer, this work formulates visual transition modeling as a style-aware temporal generation task—the first of its kind. We propose a multi-granularity style alignment mechanism and explicit temporal consistency constraints, implemented via a Transformer-based cross-modal style encoder, a differentiable transition synthesis module, and an adversarial style discriminator. Our method enables end-to-end transition recommendation and synthesis from source videos to target styles—including documentary, narrative film, or specific YouTube channel aesthetics. Evaluated on a professional video dataset, it achieves a 23.6% improvement in transition-type recommendation accuracy and attains a user preference score of 4.72/5.0—significantly outperforming state-of-the-art methods. The core contribution lies in establishing a joint style-temporal modeling paradigm that simultaneously ensures semantic coherence and visual smoothness.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Problem

• Video Style Transfer
• Natural Transition
• Specific Style Mimicry

Innovation

• V-Trans4Style
• Style Transfer
• Video Editing