PWM-ArtGen: Part World Model for Articulated Object Generation

arXiv 2026 30.2 method

TLDR

A part world model for articulated 3D object generation from a single image, learning joint visual dynamics and kinematic parameters.

Reasoning

The paper introduces a novel approach combining action and image diffusion to predict kinematic structure, addressing limitations of existing methods. Strengths include a curated dataset and strong zero-shot generalization; weaknesses include reliance on photorealistic synthetic data and lack of real-world validation.

Read-first score

Read-first score 30.2, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 21.

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,dataset,experiment

Reproducibility 18%

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

Topical relevance 29%

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: 95.

Keyword Scores

world model

world dynamics prediction

generative world model

world simulator

interactive world model

video world model

model-based reinforcement learning world model

Resources

Paper