pturing all combinations of modality-specific details across inputs, which
can inadvertently obscure the high-level semantic concepts that are shared
across modalities. Notably, Multimodal VAEs with low-dimensional latent
variables are designed to capture shared representations, enabling various
tasks such as joint multimodal synthesis and cross-modal inference. However,
multimodal VAEs often struggle to design expressive joint variational
posteriors and suffer from low-quality synthesis. In this work, ShaL