title: Error Analyses of Auto-Regressive Video Diffusion Models: A Unified
    Framework
summary: A variety of Auto-Regressive Video Diffusion Models (ARVDM) have achieved
  remarkable successes in generating realistic long-form videos. However,
  theoretical analyses of these models remain scant. In this work, we develop
  theoretical underpinnings for these models and use our insights to improve the
  performance of existing models. We first develop Meta-ARVDM, a unified
  framework of ARVDMs that subsumes most existing methods. Using Meta-ARVDM, we
  analyze the KL-divergence between the videos generated by Meta-ARVDM and the
  true videos. Our analysis uncovers two important phenomena inherent to ARVDM --
  error accumulation and memory bottleneck. By deriving an information-theoretic
  impossibility result, we show that the memory bottleneck phenomenon cannot be
  avoided. To mitigate the memory bottleneck, we design various network
  structures to explicitly use more past frames. We also achieve a significantly
  improved trade-off between the mitigation of the memory bottleneck and the
  inference efficiency by compressing the frames. Experimental results on DMLab
  and Minecraft validate the efficacy of our methods. Our experiments also
  demonstrate a Pareto-frontier between the error accumulation and memory
  bottleneck across different methods.
paper_authors._id: 67d7eba831dd5b46c3e6fdcb
paper_authors.name: Jing Wang
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paper_authors.fullname: Wang Jing
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paper_authors.name: Fengzhuo Zhang
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paper_authors.fullname: Fengzhuo Zhang
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paper_authors.user: Fengzhuo
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paper_authors.name: Xiaoli Li
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paper_authors.name: Vincent Y. F. Tan
paper_authors._id: 67d7eba831dd5b46c3e6fdcf
paper_authors.name: Tianyu Pang
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paper_authors.fullname: Tianyu Pang
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paper_authors._id: 67d7eba831dd5b46c3e6fdd0
paper_authors.name: Chao Du
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paper_authors.avatarUrl: /avatars/23b51b37b12b51a0947f687d1de4d3b5.svg
paper_authors.fullname: Chao Du
paper_authors.type: user
paper_authors.user: duchao
paper_authors._id: 67d7eba831dd5b46c3e6fdd1
paper_authors.name: Aixin Sun
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/a38455fd17bbc74ce3111f2c3da9aa59.svg
paper_authors.fullname: Aixin Sun
paper_authors.type: user
paper_authors.user: aixinsun
paper_authors._id: 67d7eba831dd5b46c3e6fdd2
paper_authors.name: Zhuoran Yang
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paper_authors.avatarUrl: /avatars/1db65fe55002ad5c137c4a59bbcd239d.svg
paper_authors.fullname: Zhuoran Yang
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paper_title: Error Analyses of Auto-Regressive Video Diffusion Models: A Unified
    Framework
paper_summary: A variety of Auto-Regressive Video Diffusion Models (ARVDM) have achieved
  remarkable successes in generating realistic long-form videos. However,
  theoretical analyses of these models remain scant. In this work, we develop
  theoretical underpinnings for these models and use our insights to improve the
  performance of existing models. We first develop Meta-ARVDM, a unified
  framework of ARVDMs that subsumes most existing methods. Using Meta-ARVDM, we
  analyze the KL-divergence between the videos generated by Meta-ARVDM and the
  true videos. Our analysis uncovers two important phenomena inherent to ARVDM --
  error accumulation and memory bottleneck. By deriving an information-theoretic
  impossibility result, we show that the memory bottleneck phenomenon cannot be
  avoided. To mitigate the memory bottleneck, we design various network
  structures to explicitly use more past frames. We also achieve a significantly
  improved trade-off between the mitigation of the memory bottleneck and the
  inference efficiency by compressing the frames. Experimental results on DMLab
  and Minecraft validate the efficacy of our methods. Our experiments also
  demonstrate a Pareto-frontier between the error accumulation and memory
  bottleneck across different methods.
paper_ai_summary: Theoretical analysis of Auto-Regressive Video Diffusion Models uncovers error accumulation and memory bottlenecks, leading to improved performance through enhanced network structures and frame compression.
paper_ai_keywords: Auto-Regressive Video Diffusion Models ARVDM Meta-ARVDM KL-divergence error accumulation memory bottleneck information-theoretic impossibility result frame compression DMLab Minecraft Pareto-frontier