Dynamic Beauty is Easy to Find: A Large-Scale Composition-Aware Dataset and an End-to-End Framework for Video Reframing

Video reframing, which converts landscape-oriented (LO) to portrait-oriented (PO) video for some PO devices such as smartphones and tablets, faces challenges. Existing approaches mainly follow a multi-step pipeline to preserve video content that ignore composition quality due to lack of large-scale datasets. To address these challenges, we propose a fully automated composition-aware dataset using vision-language models and image composition assessment models, pairing LO videos with high-quality PO versions. We then propose an end-to-end model with an attention-aware backbone and a time-aware consistency module. Experiments show our approach outperforms others in efficiency and effectiveness, proving that composition awareness and end-to-end modeling are critical for video reframing.