Structuring Prompts for AI Video Generation

Structure your prompt by prioritizing temporal consistency over spatial detail, ensuring that motion and camera logic remain stable across the entire duration of the clip. Coherent motion and scene continuity depend not on describing every visual element in the frame, but on explicitly defining how the subject moves, where the camera looks, and how the environment persists or changes over time.

Why Video Models Fail: Temporal Coherence vs. Spatial Detail

Text-to-video models struggle because they must generate a sequence of frames that look like a continuous video, rather than a series of static images. Most failures occur when the model optimizes for the visual richness of individual frames while ignoring the logical constraints of motion. If you describe a complex scene with high spatial detail—such as a crowded street with intricate architecture and multiple interacting characters—the model often allocates its computational resources to rendering those details in each frame. Consequently, the motion between frames becomes erratic, objects morph into one another, or the scene layout shifts unpredictably from one second to the next.

The core problem is that spatial detail is static, while motion is dynamic. A model that focuses on getting the texture of a building right in frame one may fail to track that building’s position in frame ten. To achieve coherence, you must accept that your prompt cannot describe every pixel of the scene. Instead, you must describe the *trajectory* of the scene. The goal is not to create a perfect static image that happens to move; the goal is to create a motion path that remains visually plausible even if the details are slightly simplified.

The Hierarchy of Video Prompt Elements

When constructing a prompt, you must establish a clear hierarchy of elements, as video models weight different aspects of the description differently. The hierarchy typically follows this order:

• Subject: Who or what is the primary focus. This must be unambiguous. If the subject is a person, define their role and state.
• Action: What the subject is doing. This is the most critical element for motion coherence.
• Environment: The setting. This should be broad and stable, not detailed and shifting.
• Camera: How the viewer perceives the scene. This dictates the frame’s composition and motion.

If you bury the action under layers of environmental description, the model will lose the thread of the motion. The prompt should lead with the motion logic, not the aesthetic details.

Describing Motion: Verbs, Direction, and Duration Cues

Motion in video prompts is defined by specific verbs, directional cues, and temporal markers. Avoid vague terms like "moves" or "acts." Instead, use precise physical descriptions.

• Verbs: Use active, physical verbs. "Walks," "runs," "turns," "raises," "drops." These imply a specific vector of motion. Passive or abstract verbs like "exists," "is present," or "expresses emotion" do not translate well into kinetic data.
• Direction: Specify the vector of motion relative to the camera or the environment. "Moves left to right across the frame" is clearer than "moves across the frame." "Turns toward the camera" is clearer than "turns around." Directionality anchors the spatial logic of the scene.
• Duration: Indicate the pace. "Slowly," "abruptly," "gradually." These terms help the model allocate temporal weight to the action. A slow action allows for more frame-to-frame consistency; a fast action risks blurring or skipping, so it requires tighter control over the start and end states.

Camera Language: Translating Cinematography Terms into Model Instructions

Video models understand standard cinematography terminology, but only if you use it as a command, not a description. Instead of saying "the scene looks like a movie," use specific camera moves.

• Static Shot: "Fixed camera," "locked-off shot." This is the most stable mode for complex scenes, as it removes camera motion from the equation, allowing the model to focus solely on subject motion.
• Tracking Shot: "Camera follows subject," "tracking shot." Use this when the subject is moving continuously. The camera’s motion must be coupled to the subject’s motion.
• Pan/Tilt: "Pan left," "tilt up." These are subtle motions useful for revealing parts of the environment without destabilizing the subject.
• Zoom: "Slow zoom in." Use with caution. Zooming changes the field of view, which can cause perspective distortion if the model misinterprets the depth of the scene.

Avoid compound camera moves in a single short clip. Combining a pan, a zoom, and a subject action often results in incoherent spatial shifts. Choose one primary camera move and keep the subject action simple.

Avoiding Temporal Drift: Anchoring the Scene Across Frames

Temporal drift occurs when the scene’s layout changes subtly from frame to frame, causing objects to vanish, shift, or morph. To prevent this, anchor the scene using static reference points.

Describe the environment using fixed, unchanging elements. "A white wall with a single red door" is more stable than "a colorful room with various decorations." The fewer the variables in the environment, the more stable the scene. If you must include dynamic elements, ensure they are secondary to the primary subject.

Additionally, define the start and end states clearly. If the clip is five seconds long, know exactly where the subject is at second zero and second five. The model interpolates between these states. If the start and end states are ambiguous, the interpolation will be chaotic.

Common Mistakes: Over-Describing the End State vs. Under-Describing the Transition

A frequent error is to describe the final outcome in high detail while neglecting the transition. For example, "The glass shatters on the floor" describes the end state but not the fall. The model may generate the glass already shattered in frame one, or the glass may vanish without falling.

Instead, describe the transition: "The glass falls from the table and shatters on the floor." This provides the kinetic path. The model must generate the fall, not just the result. Under-describing the transition leads to teleportation; over-describing the end state leads to premature appearance.

Iterative Refinement: Using Short Clips to Test Motion Logic Before Full Generation

Do not attempt to generate long, complex sequences in one pass. Start with short clips, often three to five seconds in duration, to test the motion logic.

1. Test the Subject Action: Generate a clip with only the subject’s primary action and a static camera. Check if the motion is coherent. If the subject morphs or moves unnaturally, refine the verb and direction cues.

2. Add the Camera Move: Once the subject’s motion is stable, introduce the camera movement. Check if the coupling between subject and camera is logical. If the camera drifts out of frame or the perspective shifts abruptly, simplify the camera command.

3. Refine the Environment: Finally, add environmental details. If the scene drifts or objects shift, remove details from the prompt until the stability returns.

This iterative process allows you to isolate variables. When coherence fails, you know whether the fault lies in the subject’s motion, the camera’s logic, or the environment’s complexity.

Tools like Runway, Pika, Kling, and Haiper are commonly used for this iterative process. Each is reached for different nuances in motion control or scene stability, but the underlying method remains the same: test small, refine the logic, and scale up only when the motion path is coherent. Judge the output on whether the subject’s motion remains physically plausible and whether the scene layout persists without morphing.