Learning to Generate Long-term Future Narrations Describing Activities of Daily Living

Anticipating future events is crucial for various application domains such as healthcare, smart home technology, and surveillance. Narrative event descriptions provide context-rich information, enhancing a system's future planning and decision-making capabilities. We propose a novel task: $\textit{long-term future narration generation}$, which extends beyond traditional action anticipation by generating detailed narrations of future daily activities. We introduce a visual-language model, ViNa, specifically designed to address this challenging task. ViNa integrates long-term videos and corresponding narrations to generate a sequence of future narrations that predict subsequent events and actions over extended time horizons. ViNa extends existing multimodal models that perform only short-term predictions or describe observed videos by generating long-term future narrations for a broader range of daily activities. We also present a novel downstream application that leverages the generated narrations called future video retrieval to help users improve planning for a task by visualizing the future. We evaluate future narration generation on the largest egocentric dataset Ego4D.

@article{rajendiran2025_2503.01416,
  title={ Learning to Generate Long-term Future Narrations Describing Activities of Daily Living },
  author={ Ramanathan Rajendiran and Debaditya Roy and Basura Fernando },
  journal={arXiv preprint arXiv:2503.01416},
  year={ 2025 }
}