Compositional Video Generation with MagicComp

The generation of videos from text descriptions (Text-to-Video, T2V) has made considerable progress through the use of diffusion models. However, existing methods still face challenges when it comes to precisely linking attributes, determining spatial relationships, and depicting complex action interactions between multiple subjects. A promising approach to overcome these difficulties is MagicComp, a training-free method that enhances compositional T2V generation through a two-phase refinement process.

The Two Phases of MagicComp

MagicComp is characterized by two central phases that increase the quality and accuracy of the generated videos: the conditioning phase and the denoising phase.

Conditioning Phase: Semantic Anchor Disambiguation

In the conditioning phase, so-called "Semantic Anchor Disambiguation" is used. This technique strengthens the subject-specific semantics and resolves ambiguities between the subjects. This is achieved by gradually introducing directional vectors of the semantic anchors into the original text embedding. Through this targeted influencing of the embedding, the meaning of the individual components of the text is more clearly defined and the relationship between them is specified.

Denoising Phase: Dynamic Layout Fusion Attention

The second phase, the denoising phase, utilizes "Dynamic Layout Fusion Attention." Here, grounding priors and model-specific spatial perception are integrated to flexibly bind the subjects to their spatial and temporal domains. This is achieved through modulated masked attention. The dynamic adjustment of attention allows for a more precise representation of the interactions and movements of the subjects in the video.

Model-Independent Application

A particular advantage of MagicComp lies in its model independence. The method can be seamlessly integrated into existing T2V architectures without requiring retraining of the model. This increases the flexibility and applicability of MagicComp in various contexts.

Experimental Results and Outlook

Extensive experiments on established benchmarks such as T2V-CompBench and VBench show that MagicComp surpasses the performance of existing state-of-the-art methods. The results demonstrate the potential of MagicComp for applications such as complex prompt-based and trajectory-guided video generation. The improved representation of spatial relationships and interactions between multiple subjects opens up new possibilities for creating realistic and complex videos from text descriptions.

Developments in the field of T2V generation are progressing rapidly. Methods like MagicComp contribute to pushing the boundaries of what is possible and continuously improving the quality of generated videos. Future research could focus on further optimizing semantic anchor disambiguation and dynamic layout fusion to realistically depict even more complex scenarios and action sequences.

Bibliographie: - Zhang, H., Deng, Y., Yuan, S., Jin, P., Cheng, Z., Zhao, Y., Liu, C., & Chen, J. (2025). MagicComp: Training-free Dual-Phase Refinement for Compositional Video Generation. arXiv preprint arXiv:2503.14428. - https://arxiv.org/html/2503.14428v1 - https://chatpaper.com/chatpaper/zh-CN/paper/121854 - https://www.alphaxiv.org/abs/2503.14428 - https://paperswithcode.com/task/video-generation/codeless?page=2&q= - https://ilikeafrica.com/magiccomp-training-free-dual-phase-refinement-for/ - https://github.com/yzhang2016/video-generation-survey/blob/main/video-generation.md - https://www.researchgate.net/scientific-contributions/Robin-Rombach-2174164171 - https://github.com/wangkai930418/awesome-diffusion-categorized - https://www.researchgate.net/publication/373318065_Align_Your_Latents_High-Resolution_Video_Synthesis_with_Latent_Diffusion_Models