To See a World in a Living Context: Unified Indoor-Outdoor Urban World Generation

arXiv 2026 17.1 method, system, application

TLDR

HoloWorld is a framework for unified indoor-outdoor urban 3D world generation from text, using cross-scale context to produce coherent exteriors and interiors.

Reasoning

The paper presents a novel unified generation framework with explicit indoor-outdoor correspondence and strong quantitative improvements over prior methods. However, its scope is static 3D urban generation rather than interactive or predictive world modeling, limiting direct relevance to world model or simulator keywords.

Read-first score

Read-first score 17.1, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 3.

Recency 5%

100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Reproducibility 15%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=github

Methodology quality 15%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=experiment

Graph prestige 15%

5.7

PageRank-style prestige over the local citation graph built from available OpenAlex references. local_pagerank=0.0016

Topical relevance 25%

4.3

Uses existing LLM keyword relevance scores normalized to 0-100. world model,world simulator,generative world model,interactive world model,video world model,world dynamics prediction,model-based reinforcement learning world model

Citation impact 15%

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. cited_by_count=0

Citation velocity 10%

Citation velocity estimates citations per publication-year to reduce old-paper bias. velocity=0.00

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 35.

Keyword Scores

world model

generative world model

world simulator

interactive world model

video world model

world dynamics prediction

model-based reinforcement learning world model

Resources

Paper