Text-Video Multi-Grained Integration for Video Moment Montage

• URL: http://arxiv.org/abs/2412.09276v1
• Date: Thu, 12 Dec 2024 13:40:59 GMT
• Title: Text-Video Multi-Grained Integration for Video Moment Montage
• Authors: Zhihui Yin, Ye Ma, Xipeng Cao, Bo Wang, Quan Chen, Peng Jiang,
• Abstract summary: A new task called Video Moment Montage (VMM) aims to accurately locate the corresponding video segments based on a pre-provided narration text. We present a novel textitText-Video Multi-Grained Integration method (TV-MGI) that efficiently fuses text features from the script with both shot-level and frame-level video features.
• Score: 13.794791614348084
• License:
• Abstract: The proliferation of online short video platforms has driven a surge in user demand for short video editing. However, manually selecting, cropping, and assembling raw footage into a coherent, high-quality video remains laborious and time-consuming. To accelerate this process, we focus on a user-friendly new task called Video Moment Montage (VMM), which aims to accurately locate the corresponding video segments based on a pre-provided narration text and then arrange these video clips to create a complete video that aligns with the corresponding descriptions. The challenge lies in extracting precise temporal segments while ensuring intra-sentence and inter-sentence context consistency, as a single script sentence may require trimming and assembling multiple video clips. To address this problem, we present a novel \textit{Text-Video Multi-Grained Integration} method (TV-MGI) that efficiently fuses text features from the script with both shot-level and frame-level video features, which enables the global and fine-grained alignment between the video content and the corresponding textual descriptions in the script. To facilitate further research in this area, we introduce the Multiple Sentences with Shots Dataset (MSSD), a large-scale dataset designed explicitly for the VMM task. We conduct extensive experiments on the MSSD dataset to demonstrate the effectiveness of our framework compared to baseline methods.

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