Understanding the latent causal factors of a dynamical system from visual
observations is considered a crucial step towards agents reasoning in complex
environments. In this paper, we propose CITRIS, a variational autoencoder
framework that learns causal representations from temporal sequences of images
in which underlying causal factors have possibly been intervened upon. In
contrast to the recent literature, CITRIS exploits temporality and observing
intervention targets to identify scalar and multidimensional causal factors,
such as 3D rotation angles. Furthermore, by introducing a normalizing flow,
CITRIS can be easily extended 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. Moreover, using pretrained
autoencoders, CITRIS can even generalize to unseen instantiations of causal
factors, opening future research areas in sim-to-real generalization for causal
representation learning.

本文提出了 CITRIS，一个基于变分自编码器的框架，通过观察可能受到干预的图像序列，学习掌握因果关系。与过去文献不同的是，CITRIS 结合了时间因素和观察干预目标，实现了识别标量和多维因果因素（例如 3D 旋转角度）的能力，并使用归一化流扩展到更广泛的情景。实验结果表明，CITRIS 优于以前的方法，甚至可以泛化到因果变量的新实例。