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We introduce a novel generative model for video prediction based on latentflow matching, an efficient alternative to diffusion-based models. In contrastto prior work, we keep the high costs of modeling the past during training andinference at bay by conditioning only on a small random set of past frames ateach integration step of the image generation process. Moreover, to enable thegeneration of high-resolution videos and to speed up the training, we work inthe latent space of a pretrained VQGAN. Finally, we propose to approximate theinitial condition of the flow ODE with the previous noisy frame. This allows toreduce the number of integration steps and hence, speed up the sampling atinference time. We call our model Random frame conditioned flow Integration forVidEo pRediction, or, in short, RIVER. We show that RIVER achieves superior oron par performance compared to prior work on common video predictionbenchmarks, while requiring an order of magnitude fewer computationalresources.

Efficient Video Prediction via Sparsely Conditioned Flow Matching