title: MagicComp: Training-free Dual-Phase Refinement for Compositional Video
    Generation
summary: Text-to-video (T2V) generation has made significant strides with diffusion
  models. However, existing methods still struggle with accurately binding
  attributes, determining spatial relationships, and capturing complex action
  interactions between multiple subjects. To address these limitations, we
  propose MagicComp, a training-free method that enhances compositional T2V
  generation through dual-phase refinement. Specifically, (1) During the
  Conditioning Stage: We introduce the Semantic Anchor Disambiguation to
  reinforces subject-specific semantics and resolve inter-subject ambiguity by
  progressively injecting the directional vectors of semantic anchors into
  original text embedding; (2) During the Denoising Stage: We propose Dynamic
  Layout Fusion Attention, which integrates grounding priors and model-adaptive
  spatial perception to flexibly bind subjects to their spatiotemporal regions
  through masked attention modulation. Furthermore, MagicComp is a model-agnostic
  and versatile approach, which can be seamlessly integrated into existing T2V
  architectures. Extensive experiments on T2V-CompBench and VBench demonstrate
  that MagicComp outperforms state-of-the-art methods, highlighting its potential
  for applications such as complex prompt-based and trajectory-controllable video
  generation. Project page: https://hong-yu-zhang.github.io/MagicComp-Page/.
paper_authors._id: 67e217941cb9bded659267f0
paper_authors.name: Hongyu Zhang
paper_authors.status: claimed_verified
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paper_authors.fullname: Hongyu Zhang
paper_authors.type: user
paper_authors.user: Fatpaper
paper_authors._id: 67e217941cb9bded659267f1
paper_authors.name: Yufan Deng
paper_authors.status: admin_assigned
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paper_authors.fullname: Yufan Deng
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paper_authors.user: dyf
paper_authors._id: 67e217941cb9bded659267f2
paper_authors.name: Shenghai Yuan
paper_authors.status: claimed_verified
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paper_authors.fullname: YSH
paper_authors.type: user
paper_authors.user: BestWishYsh
paper_authors._id: 67e217941cb9bded659267f3
paper_authors.name: Peng Jin
paper_authors.status: admin_assigned
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paper_authors._id: 63ad0b04e3b217fb36d36c13
paper_authors.avatarUrl: /avatars/5a3715ba20859052ba04c048db9e03c2.svg
paper_authors.fullname: Peng Jin
paper_authors.type: user
paper_authors.user: Pengjin
paper_authors._id: 67e217941cb9bded659267f4
paper_authors.name: Zesen Cheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-25T09:28:42.575Z
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paper_authors.avatarUrl: /avatars/1b09e2742aecce1bbdc57f0c4504cf38.svg
paper_authors.fullname: Zesen Cheng
paper_authors.type: user
paper_authors.user: ClownRat
paper_authors._id: 67e217941cb9bded659267f5
paper_authors.name: Yian Zhao
paper_authors._id: 67e217941cb9bded659267f6
paper_authors.name: Chang Liu
paper_authors._id: 67e217941cb9bded659267f7
paper_authors.name: Jie Chen
paper_title: MagicComp: Training-free Dual-Phase Refinement for Compositional Video
    Generation
paper_summary: Text-to-video (T2V) generation has made significant strides with diffusion
  models. However, existing methods still struggle with accurately binding
  attributes, determining spatial relationships, and capturing complex action
  interactions between multiple subjects. To address these limitations, we
  propose MagicComp, a training-free method that enhances compositional T2V
  generation through dual-phase refinement. Specifically, (1) During the
  Conditioning Stage: We introduce the Semantic Anchor Disambiguation to
  reinforces subject-specific semantics and resolve inter-subject ambiguity by
  progressively injecting the directional vectors of semantic anchors into
  original text embedding; (2) During the Denoising Stage: We propose Dynamic
  Layout Fusion Attention, which integrates grounding priors and model-adaptive
  spatial perception to flexibly bind subjects to their spatiotemporal regions
  through masked attention modulation. Furthermore, MagicComp is a model-agnostic
  and versatile approach, which can be seamlessly integrated into existing T2V
  architectures. Extensive experiments on T2V-CompBench and VBench demonstrate
  that MagicComp outperforms state-of-the-art methods, highlighting its potential
  for applications such as complex prompt-based and trajectory-controllable video
  generation. Project page: https://hong-yu-zhang.github.io/MagicComp-Page/.
paper_ai_summary: MagicComp enhances text-to-video generation by reinforcing subject semantics and spatial relationships through dual-phase refinement, demonstrating superior performance in complex prompt-based and trajectory-controllable video generation.
paper_ai_keywords: Semantic Anchor Disambiguation Dynamic Layout Fusion Attention directional vectors semantic anchors text embedding masked attention modulation grounding priors model-adaptive spatial perception