# DreamLLM-3D: Affective Dream Reliving using Large Language Model and 3D Generative AI

arXiv: https://arxiv.org/abs/2503.16439 · HTML: https://mlsanity.com/paper/2503.16439/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [Point-E: A System for Generating 3D Point Clouds from Complex Prompts](https://mlsanity.com/paper/2212.08751/) (2022)
- [Nomic Embed: Training a Reproducible Long Context Text Embedder](https://mlsanity.com/paper/2402.01613/) (2024)
- [Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features](https://mlsanity.com/paper/1611.09520/) (2016)
- [Automatic Scoring of Dream Reports' Emotional Content with Large Language Models](https://mlsanity.com/paper/2302.14828/) (2023)
- [Sequence-to-Sequence Language Models for Character and Emotion Detection in Dream Narratives](https://mlsanity.com/paper/2403.15486/) (2024)

## Built on by (most cited first, 1 of 1)

- [Coherence-Oriented Dream Scene Visualisation](https://mlsanity.com/paper/2608.05233/) (2026)

## Abstract (arXiv)

We present DreamLLM-3D, a composite multimodal AI system behind an immersive art installation for dream re-experiencing. It enables automated dream content analysis for immersive dream-reliving, by integrating a Large Language Model (LLM) with text-to-3D Generative AI. The LLM processes voiced dream reports to identify key dream entities (characters and objects), social interaction, and dream sentiment. The extracted entities are visualized as dynamic 3D point clouds, with emotional data influencing the color and soundscapes of the virtual dream environment. Additionally, we propose an experiential AI-Dreamworker Hybrid paradigm. Our system and paradigm could potentially facilitate a more emotionally engaging dream-reliving experience, enhancing personal insights and creativity.