IMPACT Improves Interaction-Aware World Model Training

Summary

IMPACT addresses a supervision imbalance in world model training by using cross-attention and local prediction errors to focus denoising on manipulated-object regions. Tests across robot-arm and human-hand manipulation show better interaction fidelity, physical plausibility, and visual quality than MSE baselines.