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Making Your Dreams A Reality: Decoding the Dreams into a Coherent Video Story from fMRI Signals

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abstract

This paper studies the brave new idea for Multimedia community, and proposes a novel framework to convert dreams into coherent video narratives using fMRI data. Essentially, dreams have intrigued humanity for centuries, offering glimpses into our subconscious minds. Recent advancements in brain imaging, particularly functional magnetic resonance imaging (fMRI), have provided new ways to explore the neural basis of dreaming. By combining subjective dream experiences with objective neurophysiological data, we aim to understand the visual aspects of dreams and create complete video narratives. Our process involves three main steps: reconstructing visual perception, decoding dream imagery, and integrating dream stories. Using innovative techniques in fMRI analysis and language modeling, we seek to push the boundaries of dream research and gain deeper insights into visual experiences during sleep. This technical report introduces a novel approach to visually decoding dreams using fMRI signals and weaving dream visuals into narratives using language models. We gather a dataset of dreams along with descriptions to assess the effectiveness of our framework.

fields

cs.AI 1

years

2026 1

verdicts

CONDITIONAL 1

representative citing papers

Coherence-Oriented Dream Scene Visualisation

cs.AI · 2026-08-05 · conditional · novelty 6.0

A dream-to-image pipeline with LLM decomposition, img2img chaining, and a CLIP feedback loop yields stylistically coherent panels while scoring lower on CLIP than a baseline, likely due to SDXL's 77-token prompt truncation.

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  • Coherence-Oriented Dream Scene Visualisation cs.AI · 2026-08-05 · conditional · none · ref 4 · internal anchor

    A dream-to-image pipeline with LLM decomposition, img2img chaining, and a CLIP feedback loop yields stylistically coherent panels while scoring lower on CLIP than a baseline, likely due to SDXL's 77-token prompt truncation.