One4D: Unified 4D Generation and Reconstruction via Decoupled LoRA Control

arXiv 2025 36.3 method

TLDR

One4D unifies 4D generation and reconstruction from sparse/full image-video inputs via decoupled LoRA control and masked conditioning, generating synchronized RGB frames and pointmaps.

Reasoning

The paper presents a strong unified framework with a novel Decoupled LoRA Control mechanism to avoid degradation in joint RGB and pointmap generation, trained on synthetic and real datasets. However, the abstract lacks quantitative results or comparisons, and the broader claim of 4D world modeling is only speculative.

Read-first score

Read-first score 36.3, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 17.

Recency 8%

86.7

Uses a gentle age decay so recent papers surface without erasing older foundations. 2025

Reproducibility 25%

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

Methodology quality 25%

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

Topical relevance 42%

24.3

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

Field roles

Frontier

Rank sensitivity

Stability: volatile; rank range: 379.

Keyword Scores

world model

video world model

generative world model

world dynamics prediction

world simulator

interactive world model

model-based reinforcement learning world model

Resources

Paper