Sora 2 - Chapter 36: ❓ FAQ

36. ❓ FAQ

Q1: What is the maximum video length in Sora 2?

A: Sora 2 currently tops out around 60 seconds. For best fidelity, craft 20–30 second segments and stitch longer narratives together in post.

Q2: How to improve face consistency?

A:

• Upload a reference photo through Cameo
• Add consistent facial features throughout to the prompt
• Specify detailed traits such as person with a round face, brown eyes, short black hair

Q3: Why is my video always blurry?

A: Run through this checklist:

• ✅ Include cues such as 4K resolution, sharp focus, high detail
• ✅ Avoid overly complex scenes (reduces processing load)
• ✅ Call out a clear subject, e.g. sharp focus on [subject]

Q4: How to avoid policy violations in generated content?

A:

• ❌ Avoid violent, explicit, or hateful content
• ❌ Skip real political figures unless for historical education
• ❌ Steer clear of misleading deepfakes
• ✅ Review OpenAI’s usage policies: https://openai.com/policies

Q5: What about copyright for commercial use?

A: Based on OpenAI policy (October 2025):

• ✅ You own the content you generate
• ⚠️ Do not infringe on third-party IP (e.g., famous characters)
• 💡 Tip: Consult legal counsel before commercial deployment

Q6: Are English prompts better than Chinese?

A:

• English: Sharper for technical terms and film/art style references
• Chinese: Excels at culture-specific concepts (e.g., “Jiangnan water town”)
• Tip: Mix languages as needed, but keep key technical cues in English

Example:

Ancient Jiangnan water town in China, misty morning,
traditional architecture, cinematic 4K

Q7: How to control randomness in generation?

A: Sora 2 doesn’t expose a seed yet, but you can:

• Generate multiple takes and pick the best
• Reduce variation by adding more detail to the prompt
• Use cues like consistent style or uniform look

Q8: Can it generate anime/cartoon styles?

A: Absolutely—try keywords such as:

• anime style, Studio Ghibli aesthetic
• Pixar 3D animation, Disney character design
• 2D cartoon, cel-shaded rendering

Q9: How to get AI to understand complex camera movement?

A: Lean on cinematic shorthand:

• Push: dolly push in, zoom in
• Pull: dolly pull out, reveal shot
• Pan: pan left/right, whip pan
• Follow: tracking shot following [subject]
• Orbit: 360-degree orbit around [subject]

Q10: What to do if generation fails or stalls?

A:

1. Simplify the prompt (overly complex scenes can fail)
2. Break the idea into several simpler shots
3. Check whether content filters were triggered
4. Retry later if servers are under heavy load

Q11: How to generate a specific brand style?

A: Borrow from established brand language:

Apple commercial style: minimalist, clean white backgrounds,
product-focused, elegant simplicity

Nike ad aesthetic: high-energy sports action,
inspirational lighting, diverse athletes, empowering mood

Coca-Cola vibe: warm nostalgic tones,
happiness moments, summer feelings, classic Americana

Q12: Can it generate text/subtitles?

A: Text generation is limited, so:

• Add subtitles in post for full control
• In-scene text (e.g., signage) can work but isn’t guaranteed
• Prompt example: readable text on sign saying "OPEN" (≈60% success)

Q13: How to make the video more "cinematic"?

A: Cinematic checklist:

• ✅ Aspect ratio: 2.39:1 anamorphic
• ✅ Frame rate: 24fps cinematic
• ✅ Color treatment: cinematic color grading
• ✅ Lighting: dramatic lighting, volumetric fog
• ✅ Camera move: smooth dolly movement
• ✅ Depth of field: shallow depth of field

Q14: What to do if character motions look unnatural?

A:

• Specify exact actions: walking naturally beats a vague moving
• Reference real-world behavior: casual conversation gestures
• Add cues like realistic body mechanics, natural movement
• Avoid extreme acrobatics—current models struggle there

Q15: How to iterate ideas quickly?

A: Efficient workflow:

1. Prototype the idea with a short prompt
2. Refine once you see the strongest direction
3. Build a prompt template library (see chapter 32)
4. Use variable scaffolding: [subject] + [action] + [environment] + [style]

Q16: Why are some styles hard to achieve?

A: Model training bias exists:

• ✅ Mainstream looks (film, advertising) land reliably
• ⚠️ Obscure art movements may be inconsistent
• 💡 Workaround: Reference well-known artists instead of abstract labels
  • ❌ Post-impressionist style
  • ✅ Vincent van Gogh painting style

Q17: How to handle unexpected artifacts or surprises?

A: Creative “errors” can become gems:

• Capture surprising wins
• Trace which keywords sparked them
• Add them to your creative library
• Embrace randomness as a tool

Real-world anecdote: A user asked for a “melting clock” and Sora turned it into a clock dissolving into a river—an unexpected idea that went viral.

Q18: How to share prompts in team collaboration?

A:

• Build a shared prompt library (Notion, Airtable, etc.)
• Standardize naming: [project]_[scene]_[version].txt
• Store metadata: generation date, creator, intended use
• Review standout examples regularly

Q19: How to evaluate prompt quality?

A: Scoring rubric:

1. Clarity (1–5): Is the description unambiguous?
2. Technical depth (1–5): Does it cover camera, lighting, etc.?
3. Creativity (1–5): Does it offer a fresh perspective?
4. Feasibility (1–5): Can today’s model deliver it?
5. Goal alignment (1–5): Does it meet the project objective?

Great prompt benchmark: 20+ out of 25

Q20: Future directions of Sora?

A: Industry forecasts point toward:

• 🔮 Longer clips (5 minutes or more)
• 🔮 Real-time generation
• 🔮 Stronger style consistency
• 🔮 Native audio generation
• 🔮 Interactive editing tools
• 🔮 Multi-modal input (sketch-to-video)

Tip: Keep an eye on official OpenAI updates

More Questions?

Join the community discussion:

• Reddit: r/SoraAI
• Discord: OpenAI Community
• Twitter: #Sora2Tips

Contributions & Feedback

Have a great prompt example? Share it with the community! Twitter: #Sora2Prompts

Disclaimer: This guide is for learning and reference only. Please follow OpenAI’s terms of use and applicable copyright regulations when using Sora 2.