Enhance Multimodal Consistency and Coherence for Text-Image Plan Generation

• URL: http://arxiv.org/abs/2506.11380v1
• Date: Fri, 13 Jun 2025 01:03:29 GMT
• Title: Enhance Multimodal Consistency and Coherence for Text-Image Plan Generation
• Authors: Xiaoxin Lu, Ranran Haoran Zhang, Yusen Zhang, Rui Zhang,
• Abstract summary: The potential of large-scale models in providing text-image plans remains understudied.<n>We propose a novel framework that generates and refines text-image plans step-by-step.<n>Our approach offers a plug-and-play improvement to various backbone models.
• Score: 8.12586545293824
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: People get informed of a daily task plan through diverse media involving both texts and images. However, most prior research only focuses on LLM's capability of textual plan generation. The potential of large-scale models in providing text-image plans remains understudied. Generating high-quality text-image plans faces two main challenges: ensuring consistent alignment between two modalities and keeping coherence among visual steps. To address these challenges, we propose a novel framework that generates and refines text-image plans step-by-step. At each iteration, our framework (1) drafts the next textual step based on the prediction history; (2) edits the last visual step to obtain the next one; (3) extracts PDDL-like visual information; and (4) refines the draft with the extracted visual information. The textual and visual step produced in stage (4) and (2) will then serve as inputs for the next iteration. Our approach offers a plug-and-play improvement to various backbone models, such as Mistral-7B, Gemini-1.5, and GPT-4o. To evaluate the effectiveness of our approach, we collect a new benchmark consisting of 1,100 tasks and their text-image pair solutions covering 11 daily topics. We also design and validate a new set of metrics to evaluate the multimodal consistency and coherence in text-image plans. Extensive experiment results show the effectiveness of our approach on a range of backbone models against competitive baselines. Our code and data are available at https://github.com/psunlpgroup/MPlanner.

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