Text2Story: Advancing Video Storytelling with Text Guidance

Generating coherent long-form video sequences from discrete input using only text prompts is a critical task in content creation. While diffusion-based models excel at short video synthesis, long-form storytelling from text remains largely unexplored and a challenge due to challenges pertaining to temporal coherency, preserving semantic meaning and action continuity across the video. We introduce a novel AI-empowered storytelling framework to enable seamless video generation with natural action transitions and structured narratives. We first present a bidirectional time-weighted latent blending strategy to ensure temporal consistency between segments of the long-form video being generated. We then introduce a dynamics-informed prompt weighting (DIPW) mechanism that adaptively adjusts the influence of scene and action prompts at each diffusion timestep by jointly considering CLIP-based alignment, narrative continuity, and temporal smoothness. To further enhance motion continuity, we propose a semantic action representation to encode high-level action semantics into the blending process, dynamically adjusting transitions based on action similarity, ensuring smooth yet adaptable motion changes. Latent space blending maintains spatial coherence between objects in a scene, while time-weighted blending enforces bidirectional constraints for temporal consistency. The resulting integrative system prevents abrupt transitions while ensuring fluid storytelling. Extensive experiments demonstrate significant improvements over baselines, achieving temporally consistent and visually compelling video narratives without any additional training. This approach bridges the gap between short clips and extended video to establish a new paradigm in GenAI-driven video synthesis from text.