DeformMaster: An Interactive Physics-Neural World Model for Deformable Objects from Videos

arXiv 2026 61.4 method, system

TLDR

DeformMaster learns an interactive physics-neural world model for deformable objects from real videos, enabling dynamics rollout and novel-view synthesis.

Reasoning

The paper presents a novel unified framework combining physics-based dynamics with neural residuals for high-fidelity modeling of deformable objects from real-world videos. Strengths include real-world experiments and outperforming baselines; weaknesses are the lack of explicit reinforcement learning context and potential complexity in scaling.

Read-first score

Read-first score 61.4, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 51.

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,experiment,metric,result

Citation impact 18%

75.2

Uses OpenAlex-shaped citation metadata as a bibliometric attention signal, separate from paper quality. citation_normalized_percentile=0.75154525

Topical relevance 29%

72.9

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

Reproducibility 18%

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

Citation velocity 12%

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

Field roles

FoundationFrontierBridgeMethodology anchor

Rank sensitivity

Stability: volatile; rank range: 396.

Keyword Scores

world model

interactive world model

video world model

world dynamics prediction

generative world model

world simulator

model-based reinforcement learning world model

Deep Analysis

Innovations

Video-derived interactive physics-neural world model for deformable objects
Preserves structured physical rollout with neural residual compensation for unmodeled effects
Grounds sparse hand motion as a distributed compliant actuator for hand-continuum interaction
Represents material response with spatially varying constitutive experts
Drives high-fidelity 4D appearance from predicted physical evolution
Unified dynamics-and-appearance framework

Methodology

DeformMaster is a physics-neural world model that combines structured physical rollout with a neural residual to compensate for unmodeled effects. It uses sparse hand motion as a distributed compliant actuator for hand-continuum interaction and represents material response with spatially varying constitutive experts. The model predicts physical evolution and renders high-fidelity 4D appearance from it, trained on real-world deformable-object videos.

Key Results

Experiments on real-world deformable-object sequences show DeformMaster outperforms state-of-the-art baselines in rolling out future dynamics and rendering dynamic appearance, while supporting novel action rollout, material-parameter variation, and dynamic novel-view synthesis.

Resources

Paper