Veo3Gen vs Luma Dream Machine Prompting: 9 “Translation Rules” to Port Any Prompt (as of 2026-02-18)

Why prompts don’t copy-paste between video models (and what to do instead)

If you’ve ever taken a prompt that looks “perfect” in Luma Dream Machine and pasted it into Veo3Gen (or the other way around), you’ve probably seen the same symptoms: the subject changes, the camera move gets ignored, the style drifts, or a single small “don’t do X” line hijacks the whole generation.

The fix isn’t memorizing two totally different prompt languages. It’s thinking like a prompt translator: preserve intent, re-express constraints, and restate the shot plan in the way the target model tends to follow.

Luma’s own guidance frames prompting as a communication problem—getting your creative vision across clearly to the model. (https://lumaai-help.freshdesk.com/support/solutions/articles/151000219614-understanding-prompting-for-dream-machine-positive-vs-negative)

Below are 9 translation rules you can use today (as of 2026-02-18) to port almost any “working Luma prompt” into a Veo3Gen-ready version. These are patterns, not feature-parity claims—use them as a repeatable workflow.

Rule #1: Start with “what must be on screen” (positive prompt first)

Luma explicitly defines positive prompting as clearly describing what you want the AI to generate. (https://lumaai-help.freshdesk.com/support/solutions/articles/151000219614-understanding-prompting-for-dream-machine-positive-vs-negative)

When translating to Veo3Gen, treat your first 1–2 lines as non-negotiable:

• Subject (who/what)
• Setting (where)
• Action (what happens)
• Style / mood / lighting (how it feels)

Luma’s best practices also recommend natural, detailed language—like a conversation with the model—and being specific about style, mood, lighting, and elements. (https://lumalabs.ai/learning-hub/best-practices)

Translation pattern: Put the “must-have visuals” up front, then add filmmaking details.

Rule #2: Convert Luma-style negatives into Veo3Gen guardrails (not a blacklist)

Positive vs negative prompting (and why negatives can backfire)

Luma defines negative prompting as instructing the AI to exclude elements from the generated video. (https://lumaai-help.freshdesk.com/support/solutions/articles/151000219614-understanding-prompting-for-dream-machine-positive-vs-negative)

But Luma also warns that negative prompting can be counterproductive—e.g., telling the model to exclude people may cause it to add people and then try to remove them. (https://lumaai-help.freshdesk.com/support/solutions/articles/151000219614-understanding-prompting-for-dream-machine-positive-vs-negative) It also notes negatives can increase the likelihood of unwanted elements appearing. (https://lumaai-help.freshdesk.com/support/solutions/articles/151000219614-understanding-prompting-for-dream-machine-positive-vs-negative)

And Luma recommends a positive-only approach for optimal results. (https://lumaai-help.freshdesk.com/support/solutions/articles/151000219614-understanding-prompting-for-dream-machine-positive-vs-negative)

Translation pattern: replace “don’t” with “do instead”

When porting to Veo3Gen, don’t carry over a long “NO / WITHOUT / EXCLUDE” list. Instead:

• Turn a negative into a positive constraint
  • “No text” → “Clean product shot, no readable text visible” (still a negative) → better: “Minimalist set design; blank labels; no signage in frame.”
• Turn ambiguity into a framing choice
  • “No people” → “Product-only shot; hands never enter frame; empty environment.”

Use negatives sparingly—more like safety rails than a blacklist.

Rule #3: Right-size your prompt (short → medium → director brief)

Some Luma prompts work because they’re breezy and conversational; others work because they’re structured. Luma’s best practices explicitly encourage natural language. (https://lumalabs.ai/learning-hub/best-practices)

Translation pattern: write your prompt in 3 layers, then stop when it works.

1. Short (1 sentence): concept only
2. Medium (3–6 lines): concept + camera + lighting + style
3. Director brief (bullets): if you need strict shot logic

When moving a prompt from Luma → Veo3Gen, start at medium. If Veo3Gen ignores something important, escalate to director brief.

Rule #4: Translate camera language into shot instructions (movement, lens, pacing)

A practical Luma-oriented order for image-to-video prompt elements is: camera type/shot, main subject, subject action, camera movement, lighting, mood. (https://filmart.ai/luma-dream-machine/)

It also gives examples of camera/shot phrasing like “360 video,” “steady camera,” and “tracking shot.” (https://filmart.ai/luma-dream-machine/)

Translation pattern: keep camera direction explicit and testable

When porting to Veo3Gen, rewrite camera language as:

• Shot size: wide / medium / close-up / macro
• Lens vibe: “wide-angle look” / “telephoto compression” (avoid over-technical if it causes misses)
• Movement: slow dolly-in, lateral tracking, handheld micro-shake, locked-off
• Pacing: “gentle, unhurried,” “snappy, energetic,” “slow-motion feel”

If a camera move is critical, repeat it once: at the top (shot line) and again near the end (reinforcement line).

Rule #5: Reference images: when 1 image is enough vs when to add a 2nd frame

Dream Machine is described as generating high-quality, realistic videos from text and images. (https://www.lummi.ai/blog/luma-labs-dream-machine)

Translation pattern: 1 image = anchor; 2 images = transition plan

When you’re doing image-to-video:

• Use 1 reference image when you want to preserve identity/style and let motion be improvised.
• Add a second image (think start/end) when you want smoother, more directed motion: “start here, end there.” The “two-frame” mindset is a simple way to reduce chaotic motion and improve continuity.

(Concept referenced from the Lummi Dream Machine overview discussing image-based creation workflows. (https://www.lummi.ai/blog/luma-labs-dream-machine))

Rule #6: Getting text inside the video: ask without breaking the shot

Luma’s best practices say you can ask for text in generations by specifying the wording, e.g., a poster with text that reads “Dream Machine.” (https://lumalabs.ai/learning-hub/best-practices)

Translation pattern: treat text as a prop, not a command

Text often fails when it’s requested like UI (“ADD CAPTIONS”). Instead, describe text as part of the scene:

• Where it appears: on a poster, label, storefront sign, lower-third card
• Typography vibe: clean sans-serif, bold, embossed, handwritten
• Legibility constraints: centered, high-contrast, not moving too fast

Also: keep the text request short and literal (exact wording), and avoid mixing it with too many other constraints.

Rule #7: Iteration loop: Brainstorm/Remix mindset → Veo3Gen variation sets

Luma’s guide emphasizes workflow tools and iteration, and notes Dream Machine retains context within a board—effectively “remembering” earlier generations and building on them. (https://lumalabs.ai/learning-hub/best-practices)

Translation pattern: batch variations with one variable at a time

When you move to Veo3Gen, mimic that iterative momentum by generating variation sets:

• Set A: same prompt, change only lighting
• Set B: same prompt, change only camera movement
• Set C: same prompt, change only style references

This gives you clean learning signals about what the model followed.

Rule #8: Common failure modes (ignored instructions, weird motion, style drift) + fixes

Ignored instructions

• Fix: move the instruction to the first line; shorten competing details.

Weird motion / body glitches

• Fix: specify “steady camera,” “gentle movement,” or “locked-off shot” (camera stability language is common in Luma prompting). (https://filmart.ai/luma-dream-machine/)

Style drift across takes

• Fix: restate the style in one consistent phrase; keep wardrobe/set constraints explicit.

Overpowered negatives

• Fix: replace negatives with positive constraints (Rule #2), since Luma notes negative prompting can increase unwanted elements. (https://lumaai-help.freshdesk.com/support/solutions/articles/151000219614-understanding-prompting-for-dream-machine-positive-vs-negative)

Prompt Translation Checklist (3 steps)

• Intent: What must the viewer understand in 2 seconds?
• Constraints: What must stay consistent (subject, setting, style, brand colors)?
• Shot plan: Shot size + camera movement + lighting + one “moment” that changes.

9 worked examples: Luma prompt → Veo3Gen prompt (with notes)

Below are translation-style rewrites. Keep your original creative idea—just restructure for clarity and controllability.

Example 1 (Product ad): espresso grinder hero shot

Luma-style prompt

Tracking shot of a premium espresso grinder on a marble countertop, morning sunlight, soft steam in background, cinematic, shallow depth of field, no people, no text.

Veo3Gen-style translation

• On screen: Premium espresso grinder centered on a marble countertop in a modern kitchen.
• Action: Subtle steam drifts in the background; grinder remains the hero.
• Camera: Slow lateral tracking shot, steady camera, close-to-medium framing.
• Lighting/mood: Warm morning sunlight, soft highlights, cinematic shallow depth-of-field.
• Constraint: Product-only environment; no hands enter frame; minimalist set with no signage.

Why this ports better: “No people” becomes “product-only environment” (positive constraint), and camera + lighting are separated into testable lines.

Example 2 (Talking-head / UGC): skincare testimonial

Luma-style prompt

A handheld selfie video of a creator in a bathroom talking about a new moisturizer, natural skin texture, casual vibe, bright soft lighting, no weird faces.

Veo3Gen-style translation

• On screen: Creator in a clean bathroom, framed chest-up, looking into the phone camera.
• Action/dialog vibe: Speaking naturally like a casual testimonial; friendly, confident energy.
• Camera: Handheld selfie feel with gentle micro-shake; stable framing (not jumpy).
• Lighting: Bright, soft, even bathroom lighting; realistic skin texture.
• Constraint: Natural facial proportions; no exaggerated expressions.

Why this ports better: “No weird faces” becomes a constraint about natural proportions without turning into a long negative list.

Example 3 (Cinematic b-roll): rainy street noir

Luma-style prompt

Cinematic tracking shot through a rainy neon street at night, reflections, moody, film look, slow motion.

Veo3Gen-style translation

• On screen: Nighttime city street in the rain; neon signs reflecting on wet pavement.
• Camera: Slow tracking shot moving forward at walking speed; steady, smooth motion.
• Look: Moody cinematic color, high contrast, filmic texture.
• Pacing: Unhurried, “slow-motion feel” (not frantic).
• Details: Raindrops, puddle ripples, occasional passing silhouettes in the distance.

Why this ports better: The shot plan is explicit (direction + speed), which helps prevent random motion.

Example 4 (On-screen text): poster reveal

Luma-style prompt

A poster on a wall that reads “SPRING SALE”, cinematic lighting, close-up, no typos.

Veo3Gen-style translation

• On screen: Close-up of a paper poster taped to a textured wall.
• Text (exact): The poster headline reads: “SPRING SALE”.
• Camera: Slow dolly-in from medium to close-up; steady camera.
• Lighting: Cinematic side light with soft shadows; high contrast for legibility.

Why this ports better: Text is treated as a physical prop with framing/lighting that supports readability (Luma notes text can be requested by specifying wording). (https://lumalabs.ai/learning-hub/best-practices)

Example 5 (Image-to-video): start/end framing for smoother motion

Luma-style prompt

Use my image, make it cinematic, camera pushes in.

Veo3Gen-style translation

• Reference image 1 (start): Match the supplied image composition and subject identity.
• Reference image 2 (end): End on a closer crop of the subject’s face with the background softly blurred.
• Camera: Smooth push-in from start frame to end frame; no sudden whip movement.
• Look: Cinematic lighting consistent across the move.

Why this ports better: The “two-frame” concept turns a vague push-in into a defined transition plan (helpful for continuity in image-based workflows). (https://www.lummi.ai/blog/luma-labs-dream-machine)

CTA: Build your own prompt translator workflow with Veo3Gen

If you’re translating prompts at scale—testing variations, automating batches, or wiring generations into your editing pipeline—explore the Veo3Gen API at /api and review plans at /pricing.

• API docs: /api
• Pricing: /pricing

FAQ

Should I keep negative prompts when moving from Luma to Veo3Gen?

Use them sparingly. Luma defines negative prompting as excluding elements, but also warns it can be counterproductive and increase unwanted elements—so translating negatives into positive constraints is often safer. (https://lumaai-help.freshdesk.com/support/solutions/articles/151000219614-understanding-prompting-for-dream-machine-positive-vs-negative)

What’s the best default structure for a translated prompt?

A reliable order is: camera/shot → subject → action → camera movement → lighting → mood (a common Luma image-to-video structure). (https://filmart.ai/luma-dream-machine/)

Can I ask for on-screen text?

Yes—Luma’s best practices explicitly say you can request text by specifying the wording (e.g., a poster that reads a phrase). (https://lumalabs.ai/learning-hub/best-practices)

Why does my camera move get ignored?

Camera directions compete with subject/style details. Put camera movement in its own line and keep it simple (e.g., “steady camera,” “tracking shot”), which is common camera language in Luma prompting examples. (https://filmart.ai/luma-dream-machine/)

Related reading

• What is the Veo 3 API?
• Veo 3 API pricing comparison
• Getting started with the Veo 3 API

Try Veo3Gen (Affordable Veo 3.1 Access)

If you want to turn these tips into real clips today, try Veo3Gen:

• Start generating via the API: /api
• See plans and pricing: /pricing