DIRECT: Video Mashup Creation via Hierarchical Multi-Agent Planning and Intent-Guided Editing
Abstract
Video mashup creation represents a complex video editing paradigm that recomposes existing footage to craft engaging audio-visual experiences, demanding intricate orchestration across semantic, visual, and auditory dimensions and multiple levels. However, existing automated editing frameworks often overlook the cross-level multimodal orchestration to achieve professional-grade fluidity, resulting in disjointed sequences with abrupt visual transitions and musical misalignment. To address this, we formulate video mashup creation as a Multimodal Coherency Satisfaction Problem (MMCSP) and propose the DIRECT framework. Simulating a professional production pipeline, our hierarchical multi-agent framework decomposes the challenge into three cascade levels: the Screenwriter for source-aware global structural anchoring, the Director for instantiating adaptive editing intent and guidance, and the Editor for intent-guided shot sequence editing with fine-grained optimization. We further introduce Mashup-Bench, a comprehensive benchmark with tailored metrics for visual continuity and auditory alignment. Extensive experiments demonstrate that DIRECT significantly outperforms state-of-the-art baselines in both objective metrics and human subjective evaluation. Project page and code: https://github.com/AK-DREAM/DIRECT
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the DIRECT framework, which innovatively addresses the complexities of video mashup creation through a hierarchical multi-agent system, significantly enhancing both the technical and creative aspects of automated video editing. The methodology effectively combines high-level narrative structuring with low-level editing precision, setting a new standard for future research in the domain.
Methodology
The paper introduces a novel framework, DIRECT, which reformulates video mashup creation as a Multimodal Coherency Satisfaction Problem (MMCSP). The hierarchical multi-agent system comprises three distinct roles: the Screenwriter for global structural alignment, the Director for segment-level cohesion, and the Editor for low-level coherency optimization. This decomposition allows for a more nuanced approach to video editing that addresses both high-level narrative coherence and low-level technical metrics. The methodology is well-structured, leveraging existing models for semantic embedding, optical flow, and saliency detection, while also introducing a closed-loop validation mechanism to adaptively refine editing intents. This innovative approach effectively bridges the gap between high-level creative intent and low-level editing precision.
Results & evaluation
The experiments are comprehensive, utilizing a newly created benchmark, Mashup-Bench, which includes a diverse set of videos and music tracks to evaluate the performance of the proposed framework against state-of-the-art baselines. The results demonstrate that DIRECT significantly outperforms existing methods in both objective metrics and subjective human evaluations, validating the effectiveness of the proposed methodology. The use of tailored metrics for visual continuity and auditory alignment is particularly noteworthy, as it provides a more relevant assessment of video mashup quality.
Reproducibility
The paper provides detailed implementation specifications, including the models used for feature extraction and the experimental setup. However, the lack of a demo URL or direct access to the generated videos limits the reproducibility of subjective evaluations. The authors do provide a GitHub repository for the project, which aids in reproducibility, but the absence of a live demo or interactive platform is a drawback.
Limitations
While the paper presents a robust framework, it does not address the potential computational costs associated with the hierarchical multi-agent system, particularly in real-time applications. Additionally, the subjective evaluation is limited to a small sample size and may not fully capture the diversity of audience preferences across different genres. The reliance on pre-trained models also raises questions about the adaptability of the system to novel or unseen footage.
Broader impact
The proposed framework has significant implications for the field of automated video editing, particularly in creative industries where high-quality content generation is essential. By enabling automated, professional-grade video mashup creation, this work could democratize access to advanced video editing capabilities, allowing non-experts to produce high-quality content. Furthermore, the introduction of a comprehensive benchmark for video editing could spur further research and development in this area. The main contribution of this paper is the DIRECT framework, which innovatively addresses the complexities of video mashup creation through a hierarchical multi-agent system, significantly enhancing both the technical and creative aspects of automated video editing. The methodology effectively combines high-level narrative structuring with low-level editing precision, setting a new standard for future research in the domain.
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