Chapter 10.2.1 - Deeplearning 2 explains teacher forcing and in particular the last section might be interesting for you:

The disadvantage of strict teacher forcing arises if the network is going to be later used in an closed-loop mode, with the network outputs (or samples from the output distribution) fed back as input. In this case, the fed-back inputs that the network sees during training could be quite different from the kind of inputs that it will see at test time. One way to mitigate this problem is to train with both teacher-forced inputs and free-running inputs, for example by predicting the correct target a number of steps in the future through the unfolded recurrent output-to-input paths. […]

and also mention references to a curriculum learning strategy.
Generally, I would skim through the literature and check how (text based) sequence models were dealing with these issues and try to adapt these techniques to your image sequences.