Video Prediction of Dynamic Physical Simulations With Pixel-Space Spatiotemporal Transformers

Personal use of this material is permitted. Abstract--Inspired by the performance and scalability of autoregressive large language models, transformer-based models have seen recent success in the visual domain. This study investigates a transformer adaptation for video prediction with a simple end-to-end approach, comparing various spatiotemporal self-attention layouts. Focusing on causal modelling of physical simulations over time; a common shortcoming of existing video-generative approaches, we attempt to isolate spatiotemporal reasoning via physical object tracking metrics and unsupervised training on physical simulation datasets. We introduce a simple yet effective pure transformer model for autoregressive video prediction, utilising continuous pixel-space representations for video prediction. Without the need for complex training strategies or latent feature-learning components, our approach significantly extends the time horizon for physically accurate predictions by up to 50% when compared with existing latent-space approaches, while maintaining comparable performance on common video quality metrics. Additionally, we conduct interpretability experiments to identify network regions that encode information useful to perform accurate estimations of PDE simulation parameters via probing models, and find this generalises to the estimation of out-of-distribution simulation parameters. This work serves as a platform for further attention-based spatiotemporal modelling of videos via a simple, parameter-efficient, and interpretable approach. ECENT progress in the development of transformer [1] based generative models, particularly text-generative models in Natural Language Processing (NLP), have led to increased efforts to extend their application beyond the linguistic domain [2, 3, 4]. Building on the success of generative modelling in the image domain, such as V ariational Autoencoders (V AEs) [5] and Diffusion models [6], recent advances have extended to generative modelling of videos. This is becoming an area of increasing research, focusing on the development of novel architectures and techniques for model interpretability [7, 4, 8].