A 1.5M-Sample Corpus Trains Video Models on Object Permanence
WROP gives video-generation researchers a 1.5M-sample corpus for training object permanence and solidity, so models can be tested on whether objects persist and behave as physical things. It uses 150 hand-designed tasks, with Blender varying lighting, speed, and camera angle while preserving each task’s cognitive structure. On a 300-question exam across 14 video models, the authors’ 16B PWM-WROP ranked first among continuation models and third overall in a blind pairwise Elo study.