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Video prediction is an important yet challenging problem; burdened with thetasks of generating future frames and learning environment dynamics. Recently,autoregressive latent video models have proved to be a powerful videoprediction tool, by separating the video prediction into two sub-problems:pre-training an image generator model, followed by learning an autoregressiveprediction model in the latent space of the image generator. However,successfully generating high-fidelity and high-resolution videos has yet to beseen. In this work, we investigate how to train an autoregressive latent videoprediction model capable of predicting high-fidelity future frames with minimalmodification to existing models, and produce high-resolution (256x256) videos.Specifically, we scale up prior models by employing a high-fidelity imagegenerator (VQ-GAN) with a causal transformer model, and introduce additionaltechniques of top-k sampling and data augmentation to further improve videoprediction quality. Despite the simplicity, the proposed method achievescompetitive performance to state-of-the-art approaches on standard videoprediction benchmarks with fewer parameters, and enables high-resolution videoprediction on complex and large-scale datasets. Videos are available athttps://sites.google.com/view/harp-videos/home.

HARP: Autoregressive Latent Video Prediction with High-Fidelity Image Generator