We propose CITRIS, a variational framework that learns causal representations from temporal sequences of images with interventions. In contrast to the recent literature, CITRIS exploits temporality and the observation of intervention targets to identify scalar and multidimensional causal factors. Furthermore, by introducing a normalizing flow, we extend CITRIS to leverage and disentangle representations obtained by already pretrained autoencoders. Extending previous results on scalar causal factors, we prove identifiability in a more general setting, in which only some components of a causal factor are affected by interventions. In experiments on 3D rendered image sequences, CITRIS outperforms previous methods on recovering the underlying causal variables, and can even generalize to unseen instantiations of causal factors, opening future research areas in sim-to-real generalization.