Stealing Creator's Workflow: A Creator-Inspired Agentic Framework with Iterative Feedback Loop for Improved Scientific Short-form Generation

• URL: http://arxiv.org/abs/2504.18805v1
• Date: Sat, 26 Apr 2025 05:22:35 GMT
• Title: Stealing Creator's Workflow: A Creator-Inspired Agentic Framework with Iterative Feedback Loop for Improved Scientific Short-form Generation
• Authors: Jong Inn Park, Maanas Taneja, Qianwen Wang, Dongyeop Kang,
• Abstract summary: SciTalk is a novel framework for grounding videos in various sources, such as text, figures, visual styles, and avatars.<n>Inspired by content creators' iterations, SciTalk uses specialized agents for content summarization, visual scene planning, and text and layout editing.<n>Our framework provides valuable insights into the challenges and benefits of feedback-driven video generation.
• Score: 20.571381061542766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating engaging, accurate short-form videos from scientific papers is challenging due to content complexity and the gap between expert authors and readers. Existing end-to-end methods often suffer from factual inaccuracies and visual artifacts, limiting their utility for scientific dissemination. To address these issues, we propose SciTalk, a novel multi-LLM agentic framework, grounding videos in various sources, such as text, figures, visual styles, and avatars. Inspired by content creators' workflows, SciTalk uses specialized agents for content summarization, visual scene planning, and text and layout editing, and incorporates an iterative feedback mechanism where video agents simulate user roles to give feedback on generated videos from previous iterations and refine generation prompts. Experimental evaluations show that SciTalk outperforms simple prompting methods in generating scientifically accurate and engaging content over the refined loop of video generation. Although preliminary results are still not yet matching human creators' quality, our framework provides valuable insights into the challenges and benefits of feedback-driven video generation. Our code, data, and generated videos will be publicly available.

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