  Verbal videos, featuring voice-overs or text overlays, provide valuable
content but present significant challenges in composition, especially when
incorporating editing effects to enhance clarity and visual appeal. In this
paper, we introduce the novel task of verbal video composition with editing
effects. This task aims to generate coherent and visually appealing verbal
videos by integrating multimodal editing effects across textual, visual, and
audio categories. To achieve this, we curate a large-scale dataset of video
effects compositions from publicly available sources. We then formulate this
task as a generative problem, involving the identification of appropriate
positions in the verbal content and the recommendation of editing effects for
these positions. To address this task, we propose VCoME, a general framework
that employs a large multimodal model to generate editing effects for video
composition. Specifically, VCoME takes in the multimodal video context and
autoregressively outputs where to apply effects within the verbal content and
which effects are most appropriate for each position. VCoME also supports
prompt-based control of composition density and style, providing substantial
flexibility for diverse applications. Through extensive quantitative and
qualitative evaluations, we clearly demonstrate the effectiveness of VCoME. A
comprehensive user study shows that our method produces videos of professional
quality while being 85$\times$ more efficient than professional editors.
