Progressive Autoregressive Video Diffusion Models

• URL: http://arxiv.org/abs/2410.08151v1
• Date: Thu, 10 Oct 2024 17:36:15 GMT
• Title: Progressive Autoregressive Video Diffusion Models
• Authors: Desai Xie, Zhan Xu, Yicong Hong, Hao Tan, Difan Liu, Feng Liu, Arie Kaufman, Yang Zhou,
• Abstract summary: We show that existing models can be naturally extended to autoregressive video diffusion models without changing the architectures. We present state-of-the-art results on long video generation at 1 minute (1440 frames at 24 FPS)
• Score: 24.97019070991881
• License: http://creativecommons.org/licenses/by/4.0/
• 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 10 seconds or 240 frames, due to computation limitations during training. In this work, we show that existing models can be naturally extended 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, which allows for fine-grained condition among the latents and large overlaps between the attention windows. Such progressive video denoising allows our models to autoregressively generate video frames without quality degradation or abrupt scene changes. We present state-of-the-art results on long video generation at 1 minute (1440 frames at 24 FPS). Videos from this paper are available at https://desaixie.github.io/pa-vdm/.

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