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arXiv · 2508.11192

Generating Dialogues from Egocentric Instructional Videos for Task Assistance: Dataset, Method and Benchmark

Lavisha Aggarwal·Vikas Bahirwani·Lin Li·Andrea Colaco
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Abstract

Many everyday tasks ranging from fixing appliances, cooking recipes to car maintenance require expert knowledge, especially when tasks are complex and multi-step. Despite growing interest in AI agents, there is a scarcity of dialogue-video datasets grounded for real world task assistance. In this paper, we propose a simple yet effective approach that transforms single-person instructional videos into task-guidance two-person dialogues, aligned with fine grained steps and video-clips. Our fully automatic approach, powered by large language models, offers an efficient alternative to the substantial cost and effort required for human-assisted data collection. Using this technique, we build HowToDIV, a large-scale dataset containing 507 conversations, 6636 question-answer pairs and 24 hours of videoclips across diverse tasks in cooking, mechanics, and planting. Each session includes multi-turn conversation where an expert teaches a novice user how to perform a task step by step, while observing user's surrounding through a camera and microphone equipped wearable device. We establish the baseline benchmark performance on HowToDIV dataset through Gemma-3 model for future research on this new task of dialogues for procedural-task assistance.

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Lavisha Aggarwal, Vikas Bahirwani, Lin Li, Andrea Colaco. 2025-08-15. Generating Dialogues from Egocentric Instructional Videos for Task Assistance: Dataset, Method and Benchmark. https://arxiv.org/abs/2508.11192

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