This model for non-visible object segmentation and content completion in videos was proposed by Carnegie Mellon University. It processes sequences of visible objects in a video using a conditional generation approach, leveraging foundational knowledge from video generation models to produce masks and RGB content that include both visible and occluded parts. The main advantages of this technology include its ability to effectively handle highly occluded situations and deformable objects. Additionally, the model outperforms existing state-of-the-art methods across multiple datasets, showing up to a 13% performance improvement in the segmentation of non-visible areas obstructed by objects.