title: AtomoVideo: High Fidelity Image-to-Video Generation
summary: Recently, video generation has achieved significant rapid development based
  on superior text-to-image generation techniques. In this work, we propose a
  high fidelity framework for image-to-video generation, named AtomoVideo. Based
  on multi-granularity image injection, we achieve higher fidelity of the
  generated video to the given image. In addition, thanks to high quality
  datasets and training strategies, we achieve greater motion intensity while
  maintaining superior temporal consistency and stability. Our architecture
  extends flexibly to the video frame prediction task, enabling long sequence
  prediction through iterative generation. Furthermore, due to the design of
  adapter training, our approach can be well combined with existing personalised
  models and controllable modules. By quantitatively and qualitatively
  evaluation, AtomoVideo achieves superior results compared to popular methods,
  more examples can be found on our project website: https://atomo-
  video.github.io/.
paper_authors._id: 65e73216f9736183791ee60a
paper_authors.name: Litong Gong
paper_authors.status: admin_assigned
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paper_authors.fullname: Litong.G
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paper_authors.user: culeao
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paper_authors.name: Yiran Zhu
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paper_authors._id: 65e73216f9736183791ee60c
paper_authors.name: Weijie Li
paper_authors._id: 65e73216f9736183791ee60d
paper_authors.name: Xiaoyang Kang
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paper_authors.fullname: KK
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paper_authors.name: Biao Wang
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paper_authors.fullname: ictwangbiao
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paper_authors.user: ictwangbiao
paper_authors._id: 65e73216f9736183791ee60f
paper_authors.name: Tiezheng Ge
paper_authors._id: 65e73216f9736183791ee610
paper_authors.name: Bo Zheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-05T20:50:07.099Z
paper_authors._id: 62c695ad5aae1c624ca992e2
paper_authors.avatarUrl: /avatars/20d10fb3338e4bd4dc59e88a18cb2617.svg
paper_authors.fullname: Bo Zheng
paper_authors.type: user
paper_authors.user: bzheng
paper_title: AtomoVideo: High Fidelity Image-to-Video Generation
paper_summary: Recently, video generation has achieved significant rapid development based
  on superior text-to-image generation techniques. In this work, we propose a
  high fidelity framework for image-to-video generation, named AtomoVideo. Based
  on multi-granularity image injection, we achieve higher fidelity of the
  generated video to the given image. In addition, thanks to high quality
  datasets and training strategies, we achieve greater motion intensity while
  maintaining superior temporal consistency and stability. Our architecture
  extends flexibly to the video frame prediction task, enabling long sequence
  prediction through iterative generation. Furthermore, due to the design of
  adapter training, our approach can be well combined with existing personalised
  models and controllable modules. By quantitatively and qualitatively
  evaluation, AtomoVideo achieves superior results compared to popular methods,
  more examples can be found on our project website: https://atomo-
  video.github.io/.
paper_ai_summary: AtomoVideo generates high-fidelity videos from images using multi-granularity injection, achieving superior motion intensity and temporal stability.
paper_ai_keywords: multi-granularity image injection temporal consistency frame prediction iterative generation adapter training personalized models controllable modules