LogicDirector: Enforcing Temporal Composition in Text-to-Video Generation

Abstract

Accurately rendering temporal composition is fundamental to text-to-video (T2V) generation. While recent efforts incorporate temporal structure for long-horizon coherence, the intrinsic temporal understanding capability of T2V models remains underexplored. We observe that current models still struggle to respect canonical operators such as *before* and *after*, usually accompanied by missing entities and incorrect action binding. In this work, we propose LogicDirector, a test-time guidance framework that enforces temporal composition through executable logical specifications. By translating text prompts into first-order logic constraints over attention-derived evidence, LogicDirector constructs a neuro-symbolic verifier to evaluate noisy latent states during diffusion sampling for entity grounding, action binding, and temporal adherence. To enforce these constraints without expensive gradient-based optimization, we introduce a gradient-free Best-of-*n* latent search strategy, which preserves the model's native diffusion dynamics while steering logical satisfaction. To systematically study operator-level temporal composition, we curate TempBench, a diagnostic benchmark spanning four canonical relations, together with a hierarchical evaluation protocol that disentangles entity existence, event realization, and temporal correctness. Experiments on CogVideoX and Wan 2.1 demonstrate that our method significantly improves temporal composition while maintaining visual fidelity.