MagicComp: Training-free Dual-Phase Refinement for Compositional Video
  Generation
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/.
67e217941cb9bded659267f0
Hongyu Zhang
claimed_verified
2025-04-15T08:01:22.487Z
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Hongyu Zhang
user
Fatpaper
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Yufan Deng
admin_assigned
2025-03-25T09:28:50.390Z
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Yufan Deng
user
dyf
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Shenghai Yuan
claimed_verified
2025-03-25T08:20:33.315Z
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YSH
user
BestWishYsh
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Peng Jin
admin_assigned
2025-03-25T09:29:15.403Z
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Peng Jin
user
Pengjin
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Zesen Cheng
admin_assigned
2025-03-25T09:28:42.575Z
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Zesen Cheng
user
ClownRat
67e217941cb9bded659267f5
Yian Zhao
67e217941cb9bded659267f6
Chang Liu
67e217941cb9bded659267f7
Jie Chen
MagicComp: Training-free Dual-Phase Refinement for Compositional Video
  Generation
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/.
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.
Semantic Anchor Disambiguation Dynamic Layout Fusion Attention directional vectors semantic anchors text embedding masked attention modulation grounding priors model-adaptive spatial perception