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This paper explores self-supervised disentangled representation learning within sequential data, focusing on separating time-independent and time-varying factors in videos. We propose a new model that breaks the usual independence assumption between those factors by explicitly accounting for the causal relationship between the static/dynamic variables and that improves the model expressivity through additional Normalizing Flows. A formal definition of the factors is proposed. This formalism leads to the derivation of sufficient conditions for the ground truth factors to be identifiable, and to the introduction of a novel theoretically grounded disentanglement constraint that can be directly and efficiently incorporated into our new framework. The experiments show that the proposed approach outperforms previous complex state-of-the-art techniques in scenarios where the dynamics of a scene are influenced by its content.
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Simon, M., Frossard, P., & De Vleeschouwer, C. (2024). Sequential Representation Learning via Static-Dynamic Conditional Disentanglement. Proceedings of ECCV 2024, Lecture Notes in Computer Science, vol 15133., 110-126. https://doi.org/10.1007/978-3-031-73226-3_7