VG-TVP: Multimodal Procedural Planning via Visually Grounded Text-Video Prompting

• URL: http://arxiv.org/abs/2412.11621v1
• Date: Mon, 16 Dec 2024 10:08:38 GMT
• Title: VG-TVP: Multimodal Procedural Planning via Visually Grounded Text-Video Prompting
• Authors: Muhammet Furkan Ilaslan, Ali Koksal, Kevin Qinhong Lin, Burak Satar, Mike Zheng Shou, Qianli Xu,
• Abstract summary: We propose the Visually Grounded Text-Video Prompting (VG-TVP) method which is a novel Multimodal Procedural Planning framework. It generates cohesive text and video procedural plans given a specified high-level objective. Our VG-TVP method outperforms unimodal baselines on the Daily-PP dataset.
• Score: 14.065845158830445
• License:
• Abstract: Large Language Model (LLM)-based agents have shown promise in procedural tasks, but the potential of multimodal instructions augmented by texts and videos to assist users remains under-explored. To address this gap, we propose the Visually Grounded Text-Video Prompting (VG-TVP) method which is a novel LLM-empowered Multimodal Procedural Planning (MPP) framework. It generates cohesive text and video procedural plans given a specified high-level objective. The main challenges are achieving textual and visual informativeness, temporal coherence, and accuracy in procedural plans. VG-TVP leverages the zero-shot reasoning capability of LLMs, the video-to-text generation ability of the video captioning models, and the text-to-video generation ability of diffusion models. VG-TVP improves the interaction between modalities by proposing a novel Fusion of Captioning (FoC) method and using Text-to-Video Bridge (T2V-B) and Video-to-Text Bridge (V2T-B). They allow LLMs to guide the generation of visually-grounded text plans and textual-grounded video plans. To address the scarcity of datasets suitable for MPP, we have curated a new dataset called Daily-Life Task Procedural Plans (Daily-PP). We conduct comprehensive experiments and benchmarks to evaluate human preferences (regarding textual and visual informativeness, temporal coherence, and plan accuracy). Our VG-TVP method outperforms unimodal baselines on the Daily-PP dataset.

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