title: Progressive Autoregressive Video Diffusion Models
summary: 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/.
paper_authors._id: 67089c42d08850e483b55c12
paper_authors.name: Desai Xie
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-11T07:54:17.886Z
paper_authors._id: 6279f597812ee439d9c8de67
paper_authors.avatarUrl: /avatars/49bea87294118e18df7b774f716af81a.svg
paper_authors.fullname: Desai Xie
paper_authors.type: user
paper_authors.user: desaix
paper_authors._id: 67089c42d08850e483b55c13
paper_authors.name: Zhan Xu
paper_authors._id: 67089c42d08850e483b55c14
paper_authors.name: Yicong Hong
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-11T10:55:47.230Z
paper_authors._id: 654ca66fe1671abcbc50a6e4
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/654ca66fe1671abcbc50a6e4/VqcnC17YcqC9Hk65_LxtE.jpeg
paper_authors.fullname: Yicong Hong
paper_authors.type: user
paper_authors.user: YicongHong
paper_authors._id: 67089c42d08850e483b55c15
paper_authors.name: Hao Tan
paper_authors._id: 67089c42d08850e483b55c16
paper_authors.name: Difan Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-11T10:55:41.875Z
paper_authors._id: 6393a754d31aec207924611b
paper_authors.avatarUrl: /avatars/51afe808f8e8a032e34ae6d7a2d6e407.svg
paper_authors.fullname: Difan Liu
paper_authors.type: user
paper_authors.user: smebliu
paper_authors._id: 67089c42d08850e483b55c17
paper_authors.name: Feng Liu
paper_authors._id: 67089c42d08850e483b55c18
paper_authors.name: Arie Kaufman
paper_authors._id: 67089c42d08850e483b55c19
paper_authors.name: Yang Zhou
paper_title: Progressive Autoregressive Video Diffusion Models
paper_summary: 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/.
paper_ai_summary: Progressive noise assignment in autoregressive video diffusion models enables high-quality generation of long videos without quality degradation or abrupt scene changes.
paper_ai_keywords: video diffusion models autoregressive video diffusion models latent frames noise levels progressive noise assignment fine-grained condition attention windows video denoising long video generation