Endoscopic video is shaped by camera movement, tissue deformation, and tool interaction, but most pretraining approaches treat these dynamics only implicitly. The authors of EndoWorld-JEPA argue that this leaves useful structure on the table for representation learning.
Their method introduces a motion-guided global-local latent world model. It is inspired by action-conditioned joint-embedding predictive architectures, which learn representations by predicting future latent states given actions. Here, the 'actions' are the motion signals derived from endoscopic video.
The paper's contribution is conceptual: make dynamics explicit in pretraining rather than incidental. The provided abstract does not include experimental results, so the practical gains remain to be seen.