Bridging Creative Intent and Visual Quality: Creator-Driven Recurrent Video Generation with Agentic Feedback Loops

• URL: http://arxiv.org/abs/2606.18591v1
• Date: Wed, 17 Jun 2026 01:39:30 GMT
• Title: Bridging Creative Intent and Visual Quality: Creator-Driven Recurrent Video Generation with Agentic Feedback Loops
• Abstract summary: We introduce CHIEF, a human-AI co-creation video generation framework that places the creator at the center of human-in-the-loop iterative video refinement.<n>The creator incorporates their creative direction by driving each iteration, while their revisions are incorporated by a specialized refiner agent.<n>To test the effectiveness of our proposed framework, we work with high school and college students with no prior filmmaking experience to create videos.
• Score: 36.169206628733484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative AI has made content creation increasingly accessible, but many AI-generated videos lack narrative coherence and creative direction, issues that become more substantial at longer durations. Unlike coding, where AI generation benefits from reliable feedback and techniques such as recurrent self-improvement, video generation requires subjective feedback about plot, scenes, and narrative, which naturally motivates approaches that incorporate human creative direction. We introduce CHIEF, a human-AI co-creation video generation framework that places the creator at the center of human-in-the-loop iterative video refinement, and supports them by providing automatic subjective feedback. The creator incorporates their creative direction by driving each iteration, while their revisions are incorporated by a specialized refiner agent. The feedback loop is generated by persona-conditioned multimodal LLMs that watch generated videos and produce subjective critique from the audience perspectives, providing feedback that self-evaluation alone cannot capture. To test the effectiveness of our proposed framework, we work with high school and college students with no prior filmmaking experience to create videos, from short 1-minute videos to a complete short 10-minute film with a complicated plot.

Related papers

• CutClaw: Agentic Hours-Long Video Editing via Music Synchronization [96.62825277039117]
  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.
  arXiv  Detail & Related papers  (2026-03-31T12:25:53Z)
• NextAds: Towards Next-generation Personalized Video Advertising [46.666292153282875]
  NextAds is a generation-based paradigm for next-generation personalized video advertising.<n>We formulate two representative tasks: personalized creative generation and personalized creative integration.<n>We conduct initial exploratory experiments, demonstrating that GenAI can generate and integrate personalized creatives with encouraging performance.
  arXiv  Detail & Related papers  (2026-03-02T17:58:07Z)
• Vidmento: Creating Video Stories Through Context-Aware Expansion With Generative Video [11.510907063539982]
  Vidmento is a tool for authoring hybrid video stories that combine captured and generated media through context-aware expansion.<n> Vidmento surfaces opportunities for story development, generates clips that blend stylistically and narratively with surrounding media, and provides controls for refinement.<n>In a study with 12 creators, Vidmento supported narrative development and exploration by systematically expanding initial materials with generative media, enabling expressive video storytelling with creative intent.
  arXiv  Detail & Related papers  (2026-01-29T17:19:06Z)
• VISTA: A Test-Time Self-Improving Video Generation Agent [45.53495043750626]
  VISTA (Video Iterative Self-improvemenT Agent) is a novel system that autonomously improves video generation through refining prompts in an iterative loop.<n>It consistently improves video quality and alignment with user intent, achieving up to 60% pairwise win rate against state-of-the-art baselines.<n>Human evaluators concur, preferring VISTA outputs in 66.4% of comparisons.
  arXiv  Detail & Related papers  (2025-10-17T17:12:08Z)
• 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)
• Stealing Creator's Workflow: A Creator-Inspired Agentic Framework with Iterative Feedback Loop for Improved Scientific Short-form Generation [20.571381061542766]
  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.
  arXiv  Detail & Related papers  (2025-04-26T05:22:35Z)
• GRADEO: Towards Human-Like Evaluation for Text-to-Video Generation via Multi-Step Reasoning [62.775721264492994]
  GRADEO is one of the first specifically designed video evaluation models.<n>It grades AI-generated videos for explainable scores and assessments through multi-step reasoning.<n> Experiments show that our method aligns better with human evaluations than existing methods.
  arXiv  Detail & Related papers  (2025-03-04T07:04:55Z)
• Llama Learns to Direct: DirectorLLM for Human-Centric Video Generation [54.561971554162376]
  We introduce DirectorLLM, a novel video generation model that employs a large language model (LLM) to orchestrate human poses within videos.<n>Our model outperforms existing ones in generating videos with higher human motion fidelity, improved prompt faithfulness, and enhanced rendered subject naturalness.
  arXiv  Detail & Related papers  (2024-12-19T03:10:26Z)
• VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention [76.3175166538482]
  VideoGen-of-Thought (VGoT) is a step-by-step framework that automates multi-shot video synthesis from a single sentence.<n>VGoT addresses three core challenges: Narrative fragmentation, visual inconsistency, and transition artifacts.<n>Combined in a training-free pipeline, VGoT surpasses strong baselines by 20.4% in within-shot face consistency and 17.4% in style consistency.
  arXiv  Detail & Related papers  (2024-12-03T08:33:50Z)
• CustomVideo: Customizing Text-to-Video Generation with Multiple Subjects [74.61964363605632]
  Current approaches for personalizing text-to-video generation suffer from tackling multiple subjects.<n>We propose CustomVideo, a novel framework that can generate identity-preserving videos with the guidance of multiple subjects.
  arXiv  Detail & Related papers  (2024-01-18T13:23:51Z)
• 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.