SKALD: Learning-Based Shot Assembly for Coherent Multi-Shot Video Creation

• URL: http://arxiv.org/abs/2503.08010v1
• Date: Tue, 11 Mar 2025 03:25:44 GMT
• Title: SKALD: Learning-Based Shot Assembly for Coherent Multi-Shot Video Creation
• Authors: Chen Yi Lu, Md Mehrab Tanjim, Ishita Dasgupta, Somdeb Sarkhel, Gang Wu, Saayan Mitra, Somali Chaterji,
• Abstract summary: We present SKALD, a multi-shot video assembly method that constructs coherent video sequences from candidate shots.<n>We tackle the exponential complexity of combining multiple shots with an efficient beam-search algorithm guided by the Learned Clip Assembly score.<n>Experiments on the VSPD and our curated MSV3C datasets show that SKALD achieves an improvement of up to 48.6% in IoU and a 43% speedup over the state-of-the-art methods.
• Score: 12.89690489768177
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present SKALD, a multi-shot video assembly method that constructs coherent video sequences from candidate shots with minimal reliance on text. Central to our approach is the Learned Clip Assembly (LCA) score, a learning-based metric that measures temporal and semantic relationships between shots to quantify narrative coherence. We tackle the exponential complexity of combining multiple shots with an efficient beam-search algorithm guided by the LCA score. To train our model effectively with limited human annotations, we propose two tasks for the LCA encoder: Shot Coherence Learning, which uses contrastive learning to distinguish coherent and incoherent sequences, and Feature Regression, which converts these learned representations into a real-valued coherence score. We develop two variants: a base SKALD model that relies solely on visual coherence and SKALD-text, which integrates auxiliary text information when available. Experiments on the VSPD and our curated MSV3C datasets show that SKALD achieves an improvement of up to 48.6% in IoU and a 43% speedup over the state-of-the-art methods. A user study further validates our approach, with 45% of participants favoring SKALD-assembled videos, compared to 22% preferring text-based assembly methods.

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