MagicComp: Training-free Dual-Phase Refinement for Compositional Video
  Generation
Authors: name: Hongyu Zhang, name: Yufan Deng, name: Shenghai Yuan, name: Peng Jin, name: Zesen Cheng, name: Yian Zhao, name: Chang Liu, name: Jie Chen

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/.