Multi-View Latent Diffusion
G. Di Giacomo, G. Franzese, Tania Cerquitelli, Carla Fabiana Chiasserini, Pietro Michiardi · 2023
Multi-view observations potentially offer a more comprehensive understanding of real-world phenomena compared to observations acquired from a single viewpoint. Existing models that utilize multi-view data often consider that all views are available during inference, but this assumption may not hold in practical scenarios. To address this limitation, we introduce MVLD, a novel method that, by employing a deterministic autoencoder and a score-based diffusion model, is capable of imputing missing views. We finally envision MVLD being used in a communication system for image transmission.