What is Text-to-Video Generation?

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Text-to-video generation extends the principles of diffusion-based image synthesis to the temporal dimension, producing short video clips from natural language prompts or reference images. The core challenge is maintaining visual consistency across frames — characters, lighting, and style must remain coherent over time — while generating realistic motion. Models are typically trained on large datasets of video-text pairs and use architectures that operate on sequences of latent frames. Leading systems include Sora (OpenAI), Runway Gen-3, Luma Dream Machine, Kling AI, and Pika Labs. Use cases include marketing video production, social media content, film pre-visualization, and interactive entertainment. Current limitations include maximum clip length, occasional temporal artifacts, and difficulty following complex motion instructions.