VideoPoet by Google

VideoPoet applies LLM architectures to video generation achieving coherent long-form content with natural language control. Researchers demonstrate language model scaling benefits extend to video domain while creators access precise narrative control. Token-based video representation enables complex editing. Text prompts generate videos with consistent storytelling, character arcs, and environmental continuity. LLM conditioning understands narrative structure producing multi-shot sequences with logical progression. Video editing through natural language instructions maintains temporal coherence. Autoregressive generation scales to longer videos while maintaining quality. Multi-modal capabilities extend to image+text and video extension. Google's published evaluations report stronger narrative understanding than comparable diffusion models. Free research release includes model architecture and training insights. Substantial compute required for inference. Advances language-video unification research paradigm. Find related tools.