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arxiv: 2604.04875 · v1 · submitted 2026-04-06 · 💻 cs.CV · cs.AI· cs.MM

DIRECT: Video Mashup Creation via Hierarchical Multi-Agent Planning and Intent-Guided Editing

Ke Li , Maoliang Li , Jialiang Chen , Jiayu Chen , Zihao Zheng , Shaoqi Wang , Xiang Chen This is my paper

Pith reviewed 2026-05-10 19:41 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.MM
keywords video mashup creationmulti-agent frameworkvideo editingmultimodal coherencyhierarchical planningintent-guided editingbenchmarking
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The pith

The DIRECT framework solves video mashup creation by using hierarchical agents to ensure multimodal coherency.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that video mashup creation can be treated as a Multimodal Coherency Satisfaction Problem and solved through a three-stage agent pipeline that mimics professional film production. Current automated approaches produce sequences with abrupt visual cuts and poor music alignment because they lack coordinated planning across global structure, editing intent, and fine details. By assigning separate agents to source-aware anchoring, adaptive intent generation, and optimized shot sequencing, the method produces smoother results. This matters for anyone who wants to repurpose existing video clips into engaging content without manual editing labor.

Core claim

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.

What carries the argument

The cascade of Screenwriter, Director, and Editor agents that progressively handle global structure, intent guidance, and fine-grained editing to satisfy multimodal coherency.

If this is right

  • The approach produces higher scores on objective metrics for visual continuity and auditory alignment.
  • Generated mashups receive better ratings in human subjective evaluations than prior methods.
  • The introduced Mashup-Bench benchmark supplies standardized metrics and test cases for future video editing research.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same role decomposition could be tested on related tasks such as automatic video summarization or trailer generation.
  • If the agents can operate with low latency, the pipeline might support interactive or real-time mashup tools.
  • The framework invites comparison with single-model end-to-end editing systems to isolate the benefit of explicit hierarchical intent passing.

Load-bearing premise

Decomposing the task into Screenwriter, Director, and Editor agents accurately simulates a professional production pipeline and achieves cross-level multimodal orchestration for coherent mashups.

What would settle it

A direct comparison experiment in which human raters score DIRECT mashups as equally or less coherent in visual transitions and audio alignment than the strongest baseline methods would falsify the performance claim.

Figures

Figures reproduced from arXiv: 2604.04875 by Jialiang Chen, Jiayu Chen, Ke Li, Maoliang Li, Shaoqi Wang, Xiang Chen, Zihao Zheng.

Figure 1
Figure 1. Figure 1: Hierarchical constraints of multimodal coherency in video mashup. (1) Global Structural Alignment of narrative [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of DIRECT. We decompose video mashup creation into three collaborative modules: the Screenwriter [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of the hierarchical planning workflow in DIRECT. The Screenwriter leverages multimodal source [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Intent-Guided Shot Sequence Editing. The Editor [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative Comparison of Low-Level Coherency. While baseline (top row) only ensures semantic relevance, our [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Case study of Footage Summarization. It decon [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
read the original 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

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper claims to address the challenge of automated video mashup creation by formulating it as a Multimodal Coherency Satisfaction Problem (MMCSP). It proposes the DIRECT framework, which uses a hierarchical multi-agent system consisting of a Screenwriter agent for source-aware global structural anchoring, a Director agent for adaptive editing intent and guidance, and an Editor agent for intent-guided shot sequence editing with fine-grained optimization. Additionally, the authors introduce Mashup-Bench, a benchmark with metrics for visual continuity and auditory alignment, and demonstrate through experiments that DIRECT outperforms state-of-the-art baselines in both objective metrics and human subjective evaluation.

Significance. If the empirical results are robust, this work could significantly advance the field of automated video editing by providing a structured, multi-level approach to achieving multimodal coherence in mashups. The introduction of a specialized benchmark is a valuable contribution that may facilitate standardized evaluation in future research on video composition and editing.

minor comments (2)
  1. [Abstract] Abstract: Consider adding a brief mention of the specific objective metrics used (e.g., for visual continuity and auditory alignment) to strengthen the summary of results.
  2. [Introduction] Introduction or §3: Ensure that the description of the three agents clearly distinguishes their roles to avoid any potential overlap in responsibilities.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary and significance assessment of our work, as well as the recommendation for minor revision. The referee's description accurately reflects the MMCSP formulation, the three-level DIRECT framework, and the Mashup-Bench contribution.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces a new formulation of video mashup creation as MMCSP and a hierarchical multi-agent framework (Screenwriter-Director-Editor) plus the Mashup-Bench benchmark, with performance claims resting on external comparisons to baselines and human evaluations. No equations, fitted parameters, or derivations are present that reduce to self-inputs by construction; the argument structure is a standard empirical proposal of a novel pipeline without load-bearing self-citations or renamings of prior results.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 3 invented entities

The central claim rests on the domain assumption that video mashup creation is best solved by formulating it as MMCSP and decomposing it into the three named agent roles; no numerical free parameters are mentioned. The three agents are invented entities introduced to structure the pipeline.

axioms (1)
  • domain assumption Video mashup creation can be formulated as a Multimodal Coherency Satisfaction Problem (MMCSP) that requires cross-level orchestration across semantic, visual, and auditory dimensions.
    This formulation is introduced in the abstract to justify the hierarchical multi-agent approach.
invented entities (3)
  • Screenwriter agent no independent evidence
    purpose: Source-aware global structural anchoring
    New agent role introduced to handle high-level planning in the framework.
  • Director agent no independent evidence
    purpose: Instantiating adaptive editing intent and guidance
    New agent role introduced to bridge planning and execution.
  • Editor agent no independent evidence
    purpose: Intent-guided shot sequence editing with fine-grained optimization
    New agent role introduced for low-level editing decisions.

pith-pipeline@v0.9.0 · 5508 in / 1473 out tokens · 67025 ms · 2026-05-10T19:41:14.846808+00:00 · methodology

discussion (0)

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Reference graph

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    De-Specify: Strictly remove proper nouns, specific actors, brand names, or real-world locations

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    Visual Invariants: Identify the constant elements across Subject, Action, Background, and Shot Type

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    Footage Analysis Report

    Vibe Extraction: Distill the environmental aesthetic and atmospheric tone into concise keywords. # Output Format - Visual Archetype: [The simplified common archetype description] - Visual Vibe: [3 keywords] E.2 Screenwriter (Summary Synthesis) # Role Footage Library Analyst: A high-level synthesizer for intelligent video editing systems, specialized in ci...

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    Keyword Distillation: Extract 10-15 high-impact cinematic keywords defining the library's visual DNA

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    Cinematic Themes

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    - Sentence 2: Defining visual features (vibe, shot type, environment)

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    Global Narrative Flow: A high-level emotional and story arc

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    # Operational Guidelines

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    intense fighting

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    section_name

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    Subject + Action/Description

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    Avoid repeating the same subjects or actions to maintain montage dynamism

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    Prior Failure

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    - Motion_Continuity_Priority: For fluid action sequences (e.g., chases, racing)

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    thought_process

    Contextual Integration: Factor in the current music section's energy level to prioritize either temporal flow or visual detail. # Output Format (Strictly JSON) { "thought_process": "Analysis of query kinetics, musical energy alignment, and profile selection logic.", "weight_profile": "Profile_Name" } E.6 Director (Rhythmic Pacing) # Role Director Agent (R...

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    Temporal Constraints: A reasonable total segment duration is within 4-16 beats and must strictly $\le$ beats_remaining

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    - Low Energy: Utilize long durations (4, 8, 16 beats) for sustained shots

    Musical Alignment: - High Energy: Utilize short durations (1-2 beats) for rapid- fire editing. - Low Energy: Utilize long durations (4, 8, 16 beats) for sustained shots. - Triple Meter (3/4): Prioritize 3 or 6-beat cuts to maintain metric synchronicity

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    thought_process

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    Semantic Alignment: Verify if the candidate matches the core subject and action of the retrieval query (apply reasonable tolerance for non-literal matches)

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    Structural Integrity: Check for visual coherence, avoiding technical glitches, jarring transitions, or artifacts

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    success": boolean, // False if all candidates fail critical criteria

    Error Feedback: If rejecting all candidates, provide specific diagnostic reasons and actionable suggestions for query re- generation or subject switching. # Output Format (Strict JSON) { "success": boolean, // False if all candidates fail critical criteria "best_candidate": int | null, // 0-based index of the chosen clip "verdict": "string", // Concise pr...

    work page