Preprint Open access
Action Forcing: Training World Models on Unsupervised Video by Recovering Underlying Egomotion Bases
Synchronised action annotations are needed to train controllable world models and these datasets remain elusive. Existing approaches make use of instrumented platforms with calibrated sensors, costly manual annotation, or latent-action models which lack grounding. We instead turn ordinary unlabelled video into action-s …