arXiv cs.AI· Aman Tyagi, Hemanth Boinpally, Jonathan Chen, Douglas Gebert, Steven Hickson·· 15 小时前AI 评分38
告别试错：面向图像到视频贴合度的智能体优化框架
Beyond Trial-and-Error: Agentic Optimization for Image-to-Video Adherence
AI 导读
研究提出“Agentic Self-Improvement”框架，将图像到视频生成重构为闭环目标导向优化：先用多模态大模型迭代优化提示词，通过 Davidsonian Scene Graph 查询保障语义贴合。
正文 · 原文
Abstract:Modern black-box Image-to-Video (I2V) models offer powerful capabilities in automated content creation, yet their lack of fine-grained control and reliability presents significant challenges in professional workflows. Their inherent stochasticity causes minor variations in textual prompts or hyperparameters to yield drastically different outputs often necessitating inefficient, brute-force trial-and-error processes. To address these limitations, we introduce the ``Agentic Self-Improvement" framework, which reframes video synthesis into a closed-loop, goal-directed optimization. Our framework systematically navigates the generation parameter space using a novel two-stage approach. In the first stage, an iterative prompt optimization loop uses a multimodal Large Language Model (mLLM) to refine the input prompt. This refinement implements two automated evaluations: Davidsonian Scene Graph (DSG) queries ensure semantic adherence, and Common Mistake Questions (CMQ) for artifact detection. At the second stage, we use Bayesian optimization to efficiently co-optimize stochastic seeds and CFG scales. This search is guided by a suite of quality metrics, including the novel Video-Text Adherence (VTA) score derived from the DSG and CMQ evaluations. Our framework significantly outperforms unguided search methods: in human preference studies, videos generated via our agentic approach were strongly preferred over baseline outputs, achieving win rates up to 69\%. This work provides a practical and extensible methodology for enhancing the predictability and control of state-of-the-art video generation models, moving the field beyond speculative curiosities toward reliable, production-ready tools.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Multimedia (cs.MM) |
| Cite as: | arXiv:2608.12290 [cs.CV] |
| (or arXiv:2608.12290v2 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2608.12290 arXiv-issued DOI via DataCite |
Submission history
From: Aman Tyagi [view email]
[v1]
Wed, 12 Aug 2026 17:35:16 UTC (1,635 KB)
[v2]
Mon, 26 Oct 2026 13:52:08 UTC (2,252 KB)
来源：arXiv cs.AI · arxiv.org