Synthesizing Compositional Videos from Text Description

Prajwal Singh, Kuldeep Kulkarni, Shanmuganathan Raman, Harsh Rangwani; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2026, pp. 6775-6784

Existing pre-trained text-to-video diffusion models can generate high-quality videos, but often struggle with misalignment between the generated content and the input text, particularly while composing scenes with multiple objects. To tackle this issue, we propose a straightforward, training-free approach for compositional video generation from text. We introduce Video-ASTAR for test-time aggregation and segregation of attention with a novel centroid loss to enhance alignment, which enables the generation of multiple objects in the scene, modeling the actions and interactions. Additionally, we extend our approach to the Multi-Action video generation setting, where only the specified action should vary across a sequence of prompts. To ensure coherent action transitions, we introduce a novel token-swapping and latent interpolation strategy. Extensive experiments and ablation studies show that our method significantly outperforms baseline methods, generating videos with improved semantic and compositional consistency alongside improved temporal coherence.