1. Scene or Video Title

Purpose: Provide a concise identifier for the clip.
Example:

makefile

CopyEdit

Title: “Modern Runway – Standard Profile Shot”

• Often just a label, so the AI and user can reference the clip easily.

• Helps keep track if generating multiple scenes.

2. Background or Environment

Purpose: Establish the setting parameters.
Key Points:

• Backdrop: color, texture, any required shape or style (e.g., “light-gray backdrop, matte finish”).

• Floor or Ground: specify color, reflectivity, and material (“nonreflective matte floor in neutral gray”).

• Ambience: any soft haze, minimal décor, or total absence of extras.

Sequence: The AI typically applies these background/setting instructions first, generating the environment as a stage for the main subject.

3. Lighting Conditions

Purpose: Describe how the scene is illuminated and how the subject will appear.
Key Points:

• Light Sources: overhead diffused, side fill lights, or backlight.

• Color Temperature: neutral white, warm glow, or slight cool tone.

• Shadows: minimal or absent. Level of softness/hardness.

Sequence: Once the environment is set, the AI sets up the lighting scheme, determining brightness levels, shadow intensities, etc.

4. Camera Setup

Purpose: Define angle, distance, and lens perspective.
Key Points:

• Position: front-facing, profile, or overhead.

• Height: near subject’s waist, chest, or eye level.

• Focal Length: e.g., “medium lens with minimal distortion.”

• Frame: full-body, three-quarter, or close-up.

Sequence: After lighting, the AI places the camera in the specified vantage point or path.

5. Camera Movement (if any)

Purpose: Describe panning, tracking, or zooming.
Key Points:

• Static or Dynamic: “Camera remains static,” or “smooth pan from left to right.”

• Speed & Duration: “slow, gentle track,” or no movement for consistency.

Sequence: The AI then decides how the camera transitions or remains locked as the subject moves.

6. Tone or Style (Optional)

Purpose: Provide a subtle “mood” or “filter” if needed.
Key Points:

• Color Grading: “neutral, natural color, no warm or cool casts.”

• Overall Mood: minimalistic fashion show, or clinical demonstration.

• No Additional Effects: if you want maximum clarity, specify “no stylized filters or cinematic flair.”

Sequence: The AI might unify the final look with a consistent style or color treatment if instructed.

7. Subject Description & Action

Purpose: Finally, define who or what is on stage and how they behave.
Key Points:

• Physical Details: Height, proportions, color, attire, or features.

• Motion: “Walking from left to right,” “standing in place,” “rotating 360 degrees.”

• Facial Expression (if relevant).

• End or Exit Behavior: “stops at center and fades out,” or “continues off camera.”

Sequence: Once the environment, lighting, and camera are set, the AI superimposes the figure. The subject’s movement and final exit or pose happen last.

Below is a generalized explanation of how Sora AI (or similarly structured text-to-video systems) might process a prompt to generate a video, based on observations and documented community usage rather than official developer documentation. Exact internal mechanisms can vary, but this outline should clarify why structured prompts are best and how they’re typically parsed:

1. Reading the Prompt Top-to-Bottom

• Natural Language Parsing: Sora reads the entire text from start to finish, segmenting it into conceptual blocks.

• Keywords & Sections: If you label portions as “Camera,” “Background,” “Lighting,” etc., Sora typically associates these blocks with specific aspects of the scene.

• Priority & Overwriting: If the prompt repeats certain instructions (e.g., you specify two different lighting schemes), Sora might either attempt to merge them or use the latest mention to overwrite previous details.

2. Environment & Background Generation

• Stage Setup: Sora first looks for statements describing the overall setting (runway, forest, city street, etc.).

• Backdrops, Floors, Textures: It references color, shape, textures, lighting instructions to shape the environment’s base geometry and texture maps.

• Minimal vs. Complex Scenes: The simpler the environment request (e.g., “light-gray, nonreflective floor”), the more consistent Sora can replicate it. Overly detailed or conflicting environment demands might degrade results.

3. Lighting Interpretation

• Global Lighting: Next, Sora interprets your instructions about overhead lights, fill lights, etc., applying them to the stage.

• Shadow & Color: The AI tries to match descriptors like “soft overhead lighting,” “warm neutral color temperature,” or “no harsh shadows.”

• Stability: Complex or contradictory lighting instructions can lead to inconsistent output, so it helps to keep them concise and uniform.

4. Camera & Movement Setup

• Angles & Positions: Sora typically looks for cues like “front-facing,” “profile,” or “camera at waist height.”

• Static vs. Dynamic: It checks if you want a static shot or minimal panning/zooming. If multiple angles are described in one prompt, the system may attempt a sequence or might get confused, risking mismatch.

• Scene Length & Shots: If you detail multiple angles, Sora sometimes merges them into a single clip with transitions or, if it’s not well-defined, it can produce unpredictable results. Many users break each angle into separate prompts for best consistency.

5. Subject & Action Rendering

• Subject Description: The AI identifies lines describing the figure or object, focusing on height, proportions, color, and any specialized details like clothing or fur.

• Motion Parsing: If the instructions say, “walks from left to right,” Sora attempts to animate the figure’s movement across the stage, referencing the environment’s geometry for collisions or shadows.

• Facial Expressions & Minor Gestures: The system tries to incorporate small details (like blinking, arm swing), though exact results can vary. Simpler instructions yield more predictable animations.

6. Additional Styling or Tone

• Look & Feel: If you mention “cinematic,” “neutral color grading,” or “fashion show vibe,” Sora can interpret it as an overlay or final color grading pass.

• Effects & Atmospherics: Indicating haze, dust, or other atmospheric elements can raise complexity. Typically, Sora merges them if they don’t conflict with earlier environment settings.

7. Final Assembly & Output

• Synthesis: After the environment, camera, lighting, and subject instructions are integrated, Sora compiles them into a final animation.

• Length & Frame Rate: The system may generate a short clip, typically a few seconds. If you specify a rough duration, Sora tries to comply.

• Resolution: Sora often has default output sizes (720p, 1080p) depending on user settings or platform constraints.

8. Why Structured Prompts Help

• Clarity: Dividing the prompt into sections for environment, lighting, camera, etc. reduces confusion.

• Avoid Overwrites: Minimizing repeated or contradictory instructions ensures fewer merges or partial overwrites.

• Incremental Complexity: Start with a simple environment/camera, then add or refine details if the system consistently handles them well.

Summary of Sora’s Prompt Workflow

1. Environment – interprets background, stage geometry, coloring.

2. Lighting – sets overhead/fill/back lights per description.

3. Camera – places or animates vantage points.

4. Subject – reads physical traits, clothing, or shape, then animates it within the environment.

5. Styling/Tone – final pass for color grading or mild post-processing.

6. Compile Output – merges everything into a short clip, typically a few seconds.

Conclusion: By understanding that Sora processes prompts in a layered, sequential manner, it’s best to keep each section clear and consistent. Then, Sora is more likely to produce stable, uniform videos across multiple subjects or angles.