Generate a complete video sequence that logically progresses from [DESCRIBE YOUR STARTING SCENE/FRAME] to [DESCRIBE YOUR ENDING SCENE/FRAME]. Maintain consistent characters, style, and narrative flow throughout the transition.

The Narrative Problem in AI Video

For all their impressive visual fidelity, current AI video generators have struggled with a fundamental creative task: telling a coherent story. Users could create stunning individual shots, but stitching them together into a logical sequence required manual editing, storyboarding, and multiple generations. The 'in-between'—the narrative glue that transforms clips into scenes—remained a manual, time-consuming process. This story gap has limited AI's utility for professionals who need complete, purposeful sequences, not just random visual effects.

How Dream Machine's New Model Works

Luma AI's latest addition to its Dream Machine platform addresses this directly. Instead of prompting with just text or a single image, users now provide two keyframes: a starting frame and an ending frame. The model's core innovation is its ability to infer and generate the logical visual progression between these two points. It analyzes the content, composition, and implied motion in both frames to create a seamless video that connects them. This is a significant shift from generative models that create videos from a single point of inspiration; this model works from a defined narrative arc, with a clear beginning and end.

In practice, a user could upload a frame of an empty coffee cup on a desk and a second frame of the same cup, full and steaming. The model would generate a video of the cup being filled. A filmmaker could provide a wide shot of a character looking at a distant mountain and a close-up of their determined face, and receive a smooth zoom-in. This control over the narrative endpoints fundamentally changes the creative workflow.

Why This Matters for Creators

The immediate impact is on speed and intentionality. Storyboarding and pre-visualization, essential but tedious steps in filmmaking, advertising, and game development, can be accelerated dramatically. Creators can now prototype scene transitions, product demonstrations, and animated sequences by simply defining the key moments. It moves AI video generation from a tool for creating assets to a tool for crafting sequences.

This also represents a technical leap in temporal coherence—the model's ability to maintain consistency and logical motion over time. By constraining the problem with two fixed points, the AI has a clearer objective, often resulting in more stable and purposeful outputs than open-ended generation. It reduces the 'randomness' that can plague AI video, giving creators more directorial control.

The New Workflow and What's Next

Accessible now through the Dream Machine platform, this feature doesn't replace text-to-video but complements it. The likely workflow involves using text prompts to generate ideal start/end frames, or editing existing images to serve as those anchors, then letting the model build the bridge. The limitation, of course, is the quality and specificity of the provided frames; garbage in, garbage out still applies.

Looking ahead, this two-point control scheme could evolve into multi-point 'path' editing, where users set several keyframes along a timeline for complex scene choreography. It also pressures competitors to move beyond single-prompt generation and offer more structured creative tools. For now, Luma has provided a straightforward solution to a pervasive problem: if you know where you want a video to start and end, the AI can finally handle the journey in between.

The Takeaway: Luma's model shifts the paradigm from generating video clips to generating video narratives. It's a tool that understands cause and effect, not just aesthetics, making AI video finally useful for planned storytelling.