One Video, One World: Turning Monocular Video into Physical 4D Scenes

arXiv 2026 23.4 method, system

TLDR

OVOW reconstructs instance-level, simulation-ready 4D mesh scenes from a single monocular video without training, using a four-stage pipeline.

Reasoning

The paper presents a novel training-free pipeline for 4D reconstruction with instance separation and physical plausibility, which is a strength. However, evaluation is limited to synthetic benchmarks, and the core contribution is about reconstruction rather than world models, making relevance to the given keywords low.

Read-first score

Read-first score 23.4, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 4.

Recency 6%

100

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

Methodology quality 18%

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

Reproducibility 18%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=none

Topical relevance 29%

5.7

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 18%

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

Citation velocity 12%

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

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 43.

Keyword Scores

video world model

world model

world dynamics prediction

world simulator

generative world model

interactive world model

model-based reinforcement learning world model

Resources

Paper