Progressive Autoregressive Video Diffusion Models
Keywords: Long Video Generation, Diffusion Models, Transformer, Autoregression
TL;DR: We empower video diffusion models to autoregressively generate long videos.
Abstract: Current frontier video diffusion models have demonstrated remarkable results at
generating high-quality videos. However, they can only generate short video clips,
normally around 5 seconds or 120 frames, due to computation limitations during
training. In this work, we show that existing models can be naturally adapted to
autoregressive video diffusion models without changing the architectures. Our
key idea is to assign the latent frames with progressively increasing noise levels
rather than a single noise level. Thus, each latent can condition on all the less
noisy latents before it and provide condition for all the more noisy latents after it.
Such progressive video denoising allows our models to autoregressively generate
frames without quality degradation. We present state-of-the-art results on long
video generation at 1 minute (1440 frames at 24 FPS). Our results are available
at this anonymous url: https://progressive-autoregressive-vdm.github.io/.
Primary Area: generative models
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