title: Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation
summary: Despite diffusion models having shown powerful abilities to generate
  photorealistic images, generating videos that are realistic and diverse still
  remains in its infancy. One of the key reasons is that current methods
  intertwine spatial content and temporal dynamics together, leading to a notably
  increased complexity of text-to-video generation (T2V). In this work, we
  propose HiGen, a diffusion model-based method that improves performance by
  decoupling the spatial and temporal factors of videos from two perspectives,
  i.e., structure level and content level. At the structure level, we decompose
  the T2V task into two steps, including spatial reasoning and temporal
  reasoning, using a unified denoiser. Specifically, we generate spatially
  coherent priors using text during spatial reasoning and then generate
  temporally coherent motions from these priors during temporal reasoning. At the
  content level, we extract two subtle cues from the content of the input video
  that can express motion and appearance changes, respectively. These two cues
  then guide the model's training for generating videos, enabling flexible
  content variations and enhancing temporal stability. Through the decoupled
  paradigm, HiGen can effectively reduce the complexity of this task and generate
  realistic videos with semantics accuracy and motion stability. Extensive
  experiments demonstrate the superior performance of HiGen over the
  state-of-the-art T2V methods.
paper_authors._id: 657291e1379284b904a705c7
paper_authors.name: Zhiwu Qing
paper_authors._id: 657291e1379284b904a705c8
paper_authors.name: Shiwei Zhang
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paper_authors.fullname: Shiwei Zhang
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paper_authors.user: StevenZhang
paper_authors._id: 657291e1379284b904a705c9
paper_authors.name: Jiayu Wang
paper_authors.status: admin_assigned
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paper_authors.fullname: Jiayu Wang
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paper_authors.user: ArseneRean
paper_authors._id: 657291e1379284b904a705ca
paper_authors.name: Xiang Wang
paper_authors._id: 657291e1379284b904a705cb
paper_authors.name: Yujie Wei
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-11T07:44:49.668Z
paper_authors._id: 637f70d6fab5db9101c3dfc8
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paper_authors.fullname: Yujie Wei
paper_authors.type: user
paper_authors.user: weilllllls
paper_authors._id: 657291e1379284b904a705cc
paper_authors.name: Yingya Zhang
paper_authors._id: 657291e1379284b904a705cd
paper_authors.name: Changxin Gao
paper_authors._id: 657291e1379284b904a705ce
paper_authors.name: Nong Sang
paper_title: Hierarchical Spatio-temporal Decoupling for Text-to-Video Generation
paper_summary: Despite diffusion models having shown powerful abilities to generate
  photorealistic images, generating videos that are realistic and diverse still
  remains in its infancy. One of the key reasons is that current methods
  intertwine spatial content and temporal dynamics together, leading to a notably
  increased complexity of text-to-video generation (T2V). In this work, we
  propose HiGen, a diffusion model-based method that improves performance by
  decoupling the spatial and temporal factors of videos from two perspectives,
  i.e., structure level and content level. At the structure level, we decompose
  the T2V task into two steps, including spatial reasoning and temporal
  reasoning, using a unified denoiser. Specifically, we generate spatially
  coherent priors using text during spatial reasoning and then generate
  temporally coherent motions from these priors during temporal reasoning. At the
  content level, we extract two subtle cues from the content of the input video
  that can express motion and appearance changes, respectively. These two cues
  then guide the model's training for generating videos, enabling flexible
  content variations and enhancing temporal stability. Through the decoupled
  paradigm, HiGen can effectively reduce the complexity of this task and generate
  realistic videos with semantics accuracy and motion stability. Extensive
  experiments demonstrate the superior performance of HiGen over the
  state-of-the-art T2V methods.
paper_ai_summary: HiGen is a diffusion model-based method that decouples spatial and temporal factors to improve text-to-video generation, achieving superior realism and motion stability.
paper_ai_keywords: diffusion models text-to-video generation T2V spatial reasoning temporal reasoning denoiser spatially coherent priors temporally coherent motions motion and appearance changes content variations temporal stability