CutClaw: Agentic Hours-Long Video Editing via Music Synchronization

• URL: http://arxiv.org/abs/2603.29664v1
• Date: Tue, 31 Mar 2026 12:25:53 GMT
• Title: CutClaw: Agentic Hours-Long Video Editing via Music Synchronization
• Abstract summary: CutClaw is an autonomous multi-agent framework designed to edit hours-long raw footage into meaningful short videos.<n>It produces videos with synchronized music, followed by instructions, and a visually appealing appearance.<n>We conduct detailed experiments to demonstrate that CutClaw significantly outperforms state-of-the-art baselines in generating high-quality, rhythm-aligned videos.
• Score: 96.62825277039117
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Editing the video content with audio alignment forms a digital human-made art in current social media. However, the time-consuming and repetitive nature of manual video editing has long been a challenge for filmmakers and professional content creators alike. In this paper, we introduce CutClaw, an autonomous multi-agent framework designed to edit hours-long raw footage into meaningful short videos that leverages the capabilities of multiple Multimodal Language Models~(MLLMs) as an agent system. It produces videos with synchronized music, followed by instructions, and a visually appealing appearance. In detail, our approach begins by employing a hierarchical multimodal decomposition that captures both fine-grained details and global structures across visual and audio footage. Then, to ensure narrative consistency, a Playwriter Agent orchestrates the whole storytelling flow and structures the long-term narrative, anchoring visual scenes to musical shifts. Finally, to construct a short edited video, Editor and Reviewer Agents collaboratively optimize the final cut via selecting fine-grained visual content based on rigorous aesthetic and semantic criteria. We conduct detailed experiments to demonstrate that CutClaw significantly outperforms state-of-the-art baselines in generating high-quality, rhythm-aligned videos. The code is available at: https://github.com/GVCLab/CutClaw.

Related papers

• ViMax: Agentic Video Generation [55.546794487716646]
  Long-form video generation requires systematic narrative planning and visual consistency.<n>Our framework employs a hierarchical narrative engine with retrieval-augmented generation for global story coherence.<n>VLM-guided agents continuously monitor and refine both narrative coherence and visual fidelity.
  arXiv  Detail & Related papers  (2026-06-02T12:55:41Z)
• EditYourself: Audio-Driven Generation and Manipulation of Talking Head Videos with Diffusion Transformers [3.3508228801277853]
  We introduce EditYourself, a DiTT-based framework for audio-driven video-to-videoV editing.<n>It enables transcript-based modification of talking videos, including the seamless addition, removal, and retiming of visually spoken content.<n>This represents a step toward generative video models as practical tools for professional video post-production.
  arXiv  Detail & Related papers  (2026-01-29T18:49:27Z)
• Prompt-Driven Agentic Video Editing System: Autonomous Comprehension of Long-Form, Story-Driven Media [35.60423976124236]
  We present a prompt-driven, modular editing system that helps creators restructure multi-hour content through free-form prompts rather than timelines.<n>At its core is a semantic indexing pipeline that builds a global narrative via temporal segmentation, guided memory compression, and cross-granularity fusion.<n>Our system scales prompt-driven editing, preserves narrative coherence, and balances automation with creator control.
  arXiv  Detail & Related papers  (2025-09-20T21:22:56Z)
• MAViS: A Multi-Agent Framework for Long-Sequence Video Storytelling [24.22367257991941]
  MAViS is a multi-agent collaborative framework designed to assist in long-sequence video storytelling.<n>It orchestrates specialized agents across multiple stages, including script writing, shot designing, character modeling, generation, video animation, and audio generation.<n>With just a brief idea description, MAViS enables users to rapidly explore diverse visual storytelling and creative directions for sequential video generation by efficiently producing high-quality, complete long-sequence videos.
  arXiv  Detail & Related papers  (2025-08-11T21:42:41Z)
• Captain Cinema: Towards Short Movie Generation [66.22442526026215]
  We present Captain Cinema, a generation framework for short movie generation.<n>Our approach generates a sequence of synthesiss that outline the entire narrative.<n>Our model is trained on a specially curated dataset consisting of interleaved data pairs.
  arXiv  Detail & Related papers  (2025-07-24T17:59:56Z)
• From Long Videos to Engaging Clips: A Human-Inspired Video Editing Framework with Multimodal Narrative Understanding [16.83482677439144]
  We propose a human-inspired automatic video editing framework (HIVE)<n>Our approach incorporates character extraction, dialogue analysis, and narrative summarization through multimodal large language models.<n>Our framework consistently outperforms existing baselines across both general and advertisement-oriented editing tasks.
  arXiv  Detail & Related papers  (2025-07-03T16:54:32Z)
• AniMaker: Multi-Agent Animated Storytelling with MCTS-Driven Clip Generation [50.63646953706144]
  We introduce AniMaker, a framework enabling efficient multi-candidate clip generation and storytelling-aware clip selection.<n>AniMaker achieves superior quality as measured by popular metrics including VBench and our proposed AniEval framework.
  arXiv  Detail & Related papers  (2025-06-12T10:06:21Z)
• Long-Video Audio Synthesis with Multi-Agent Collaboration [20.332328741375363]
  LVAS-Agent is a novel framework that emulates professional dubbing through collaborative role.<n>Our approach decomposes long-video synthesis into four steps including scene segmentation, script generation, sound design and audio synthesis.<n>Central innovations include a discussion-correction mechanism for scene/script refinement and a generation-retrieval loop for temporal-semantic alignment.
  arXiv  Detail & Related papers  (2025-03-13T07:58:23Z)
• MovieDreamer: Hierarchical Generation for Coherent Long Visual Sequence [62.72540590546812]
  MovieDreamer is a novel hierarchical framework that integrates the strengths of autoregressive models with diffusion-based rendering. We present experiments across various movie genres, demonstrating that our approach achieves superior visual and narrative quality.
  arXiv  Detail & Related papers  (2024-07-23T17:17:05Z)
• SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction [93.26613503521664]
  This paper presents a short-to-long video diffusion model, SEINE, that focuses on generative transition and prediction. We propose a random-mask video diffusion model to automatically generate transitions based on textual descriptions. Our model generates transition videos that ensure coherence and visual quality.
  arXiv  Detail & Related papers  (2023-10-31T17:58:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.