title: DropletVideo: A Dataset and Approach to Explore Integral Spatio-Temporal
    Consistent Video Generation
summary: Spatio-temporal consistency is a critical research topic in video generation.
  A qualified generated video segment must ensure plot plausibility and coherence
  while maintaining visual consistency of objects and scenes across varying
  viewpoints. Prior research, especially in open-source projects, primarily
  focuses on either temporal or spatial consistency, or their basic combination,
  such as appending a description of a camera movement after a prompt without
  constraining the outcomes of this movement. However, camera movement may
  introduce new objects to the scene or eliminate existing ones, thereby
  overlaying and affecting the preceding narrative. Especially in videos with
  numerous camera movements, the interplay between multiple plots becomes
  increasingly complex. This paper introduces and examines integral
  spatio-temporal consistency, considering the synergy between plot progression
  and camera techniques, and the long-term impact of prior content on subsequent
  generation. Our research encompasses dataset construction through to the
  development of the model. Initially, we constructed a DropletVideo-10M dataset,
  which comprises 10 million videos featuring dynamic camera motion and object
  actions. Each video is annotated with an average caption of 206 words,
  detailing various camera movements and plot developments. Following this, we
  developed and trained the DropletVideo model, which excels in preserving
  spatio-temporal coherence during video generation. The DropletVideo dataset and
  model are accessible at https://dropletx.github.io.
paper_authors._id: 67cfd2d7bc539099da9ebecb
paper_authors.name: Runze Zhang
paper_authors._id: 67cfd2d7bc539099da9ebecc
paper_authors.name: Guoguang Du
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paper_authors.fullname: GeorgeDu
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paper_authors.user: georgedu
paper_authors._id: 67cfd2d7bc539099da9ebecd
paper_authors.name: Xiaochuan Li
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paper_authors.fullname: Xiaochuan Li
paper_authors.type: user
paper_authors.user: lixiaochuan
paper_authors._id: 67cfd2d7bc539099da9ebece
paper_authors.name: Qi Jia
paper_authors._id: 67cfd2d7bc539099da9ebecf
paper_authors.name: Liang Jin
paper_authors._id: 67cfd2d7bc539099da9ebed0
paper_authors.name: Lu Liu
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paper_authors.fullname: Ellen Liu
paper_authors.type: user
paper_authors.user: EllenAP
paper_authors._id: 67cfd2d7bc539099da9ebed1
paper_authors.name: Jingjing Wang
paper_authors._id: 67cfd2d7bc539099da9ebed2
paper_authors.name: Cong Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-18T09:16:48.232Z
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paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/-tfSq05d4nkLkU_E-N75e.png
paper_authors.fullname: Cong Xu
paper_authors.type: user
paper_authors.user: NeilXu
paper_authors._id: 67cfd2d7bc539099da9ebed3
paper_authors.name: Zhenhua Guo
paper_authors._id: 67cfd2d7bc539099da9ebed4
paper_authors.name: Yaqian Zhao
paper_authors._id: 67cfd2d7bc539099da9ebed5
paper_authors.name: Xiaoli Gong
paper_authors._id: 67cfd2d7bc539099da9ebed6
paper_authors.name: Rengang Li
paper_authors._id: 67cfd2d7bc539099da9ebed7
paper_authors.name: Baoyu Fan
paper_title: DropletVideo: A Dataset and Approach to Explore Integral Spatio-Temporal
    Consistent Video Generation
paper_summary: Spatio-temporal consistency is a critical research topic in video generation.
  A qualified generated video segment must ensure plot plausibility and coherence
  while maintaining visual consistency of objects and scenes across varying
  viewpoints. Prior research, especially in open-source projects, primarily
  focuses on either temporal or spatial consistency, or their basic combination,
  such as appending a description of a camera movement after a prompt without
  constraining the outcomes of this movement. However, camera movement may
  introduce new objects to the scene or eliminate existing ones, thereby
  overlaying and affecting the preceding narrative. Especially in videos with
  numerous camera movements, the interplay between multiple plots becomes
  increasingly complex. This paper introduces and examines integral
  spatio-temporal consistency, considering the synergy between plot progression
  and camera techniques, and the long-term impact of prior content on subsequent
  generation. Our research encompasses dataset construction through to the
  development of the model. Initially, we constructed a DropletVideo-10M dataset,
  which comprises 10 million videos featuring dynamic camera motion and object
  actions. Each video is annotated with an average caption of 206 words,
  detailing various camera movements and plot developments. Following this, we
  developed and trained the DropletVideo model, which excels in preserving
  spatio-temporal coherence during video generation. The DropletVideo dataset and
  model are accessible at https://dropletx.github.io.
paper_ai_keywords: spatio-temporal consistency video generation plot plausibility visual consistency objects scenes viewpoints camera movement prompt narrative plot progression camera techniques long-term impact dataset construction DropletVideo-10M dataset dynamic camera motion object actions caption DropletVideo model spatio-temporal coherence