AI Video Character Consistency: Keep the Same Face Across Every Scene

Your character looked perfect in scene one — then became a different person in scene two. Here's how to fix that in 2026.

Why Character Consistency Is AI Video's Biggest Remaining Challenge

AI video generation has solved resolution. It's solved physics. It's even solved synchronized audio. But ask any creator what still breaks their projects — alongside common AI video artifacts — and you'll hear the same answer: character consistency.

You spend 30 minutes crafting the perfect character in scene one. Dark hair, blue jacket, sharp jawline. Scene two loads, and suddenly your protagonist has lighter hair, a different jacket, and a softer face. The scene looks great on its own — but it's clearly a different person.

This matters because storytelling requires identity. An ad campaign needs the same spokesperson across 10 variations. A short drama needs the same protagonist across 20 episodes. A product demo needs the same presenter from intro to CTA. Without character consistency, AI video remains limited to single-shot content.

The good news: 2026 changed everything. Character consistency has evolved from a bleeding-edge experiment to a baseline feature. Every major model now offers some form of cross-shot character coherence. The question isn't whether you can do it — it's which method works best for your project.

4 Methods to Maintain Character Identity

There are four practical approaches to character consistency in AI video, each suited to different scenarios. The key breakthrough is separating identity creation from motion creation.

Method 1: Start Frame (Single Reference Image)

The simplest approach. Provide a single image of your character as the first frame, then let the model generate motion from that starting point.

How it works: Upload a reference image of your character. The model uses it as the visual anchor and generates video from that frame forward.

Best for: Quick single-scene generation where you need a specific look. Product presenters, talking heads, simple character introductions.

Limitation: Identity can drift if the motion is too complex or the prompt diverges too far from the reference. Works best for short clips (5-10 seconds) with moderate movement.

Method 2: Keyframe Interpolation (Start + End Frames)

Define both the beginning and end state of your character, and let the model interpolate the motion between them.

How it works: Provide two reference frames — where the character starts and where they end up. The model generates smooth motion between these anchor points while preserving the character's identity at both endpoints.

Best for: Controlled character movement where you know the start and end positions. Walking sequences, seated-to-standing transitions, turning motions.

Limitation: Requires more preparation (two reference images per shot). The model may take creative liberties with the in-between motion.

Method 3: Re-Roll with Modify (Keep Structure, Change Details)

Generate a base video, then modify specific elements while preserving the character's core identity and motion structure.

How it works: Generate your initial video. Then use the model's modify or edit function to regenerate with changes — new background, different lighting, adjusted camera angle — while locking the character's appearance. Luma's Ray3 offers "precise keyframe and character reference controls" for this workflow.

Best for: Creating multiple variations of the same scene for A/B testing. Adapting a character to different settings or contexts. Fine-tuning after initial generation.

Limitation: Each re-roll introduces small variations. After 3-4 iterations, subtle drift may accumulate. Save your best version early.

Method 4: Separate Compositing (Character + Background Independently)

Generate the character animation and the background separately, then composite them in post-production.

How it works: Animate your character against a clean or green background. Generate your background environment separately. Composite the layers in an editor.

Best for: Maximum control over character consistency. Complex scenes where the environment changes but the character must remain identical. Professional productions with editing capability.

Limitation: Requires more manual work and basic compositing skills. Lighting matching between character and background can be tricky.

Model-by-Model Comparison: Who Does It Best?

Every major AI video model now offers character consistency features, but their approaches and strengths differ significantly.

Kling 3.0: Storyboard Mode

Kling 3.0's standout feature is its storyboard mode, which generates up to 6 camera cuts in a single generation with automatic visual consistency across cuts. You describe a sequence — "character walks into cafe, sits down, orders coffee, looks out window" — and Kling produces a coherent multi-shot sequence.

The 4K native resolution (3840x2160 at 60fps) means each cut is broadcast-quality. For single-generation multi-scene work, Kling 3.0 is currently the strongest option.

Best for: Multi-shot sequences generated in one pass. Storyboard-driven projects. High-resolution output requirements.

Seedance 2.0: Identity-Lock System

Seedance 2.0 approaches consistency differently with its identity-lock system. Upload reference images of your character, and the model locks onto their identity across separate generations. This means you can generate scenes days apart and maintain the same character.

The system supports multi-modal reference inputs — face photos, full-body images, clothing references — giving you fine-grained control over what stays consistent.

Best for: Long-form projects where scenes are generated over time. Character-driven series content. Projects requiring the same character across many different scenarios.

Runway Gen-4.5: Character Persistence

Runway's character persistence feature takes a creative-tool approach. Build a character profile within the platform, and it persists across all your generations. Combined with Runway's industry-leading creative controls and choreography understanding, this makes it powerful for precise directorial work.

Best for: Professional production workflows. Projects requiring precise camera and movement control alongside character consistency. Film-style content.

Sora 2: Multi-Character Storytelling

Sora 2 approaches video generation as storytelling. Where other models focus on single-character identity, Sora excels at multi-character scenes. Five people in a room, each performing distinct actions — Sora produces coherent output more reliably than competitors.

Best for: Scenes with multiple interacting characters. Narrative-driven content. Complex social scenarios — conversations, group activities, crowd scenes.

Comparison Table

• Kling 3.0 — Up to 6 cuts/generation, storyboard mode, 4K/60fps. Best: single-generation multi-shot.
• Seedance 2.0 — Identity-lock, multi-modal reference, cross-session persistence. Best: long-form character series.
• Runway Gen-4.5 — Character profiles, choreography control, creative toolkit. Best: professional direction.
• Sora 2 — Multi-character coherence, storytelling engine, natural interactions. Best: scenes with 3+ characters.

The Genra Approach: Let the Agent Pick the Best Model Per Scene

Here's the truth about character consistency in 2026: no single model is best for every scene. Kling dominates multi-shot sequences. Seedance excels at identity-lock across sessions. Sora handles multi-character interactions. Runway gives you the most creative control. (For a full feature-by-feature breakdown, see our 4-model comparison guide.)

A real production project — a 12-episode short drama, a 10-variation ad campaign, a product demo series — will need different models for different scenes. Managing character consistency across multiple models manually is a nightmare of reference images, export settings, and format conversions.

This is where Genra's agent approach changes the game. Describe your project in natural language — the characters, the scenes, the style. Genra's agent automatically selects the best model for each scene type, maintains your character references across all of them, and delivers a consistent final product.

You don't manage the models. You don't track reference images. You don't convert between formats. The agent handles it all. One prompt, one consistent output, regardless of how many models were used behind the scenes.

Step-by-Step: Create a 6-Scene Story with Consistent Characters

Let's walk through a practical workflow for creating a short narrative with consistent characters.

1. Define your character — Create or find 3-5 reference images of your character from different angles. Front-facing, three-quarter, profile. Clear lighting, clean backgrounds. At least 1024x1024 resolution.
2. Create a style guide — Document your character's key features in text: hair color and style, eye color, clothing, distinguishing marks. This serves as both a prompt reference and a consistency checkpoint.
3. Plan your shots — Outline 6 scenes with brief descriptions. Include camera angle, action, setting, and mood for each. Think of it as a simple storyboard in text form.
4. Generate the anchor scene — Start with your most important scene (usually the close-up or hero shot). This becomes the visual anchor that all other scenes reference.
5. Generate remaining scenes — Using your anchor scene as the primary reference, generate the remaining 5 scenes. Include your character reference images and style guide text in each prompt.
6. Review and regenerate — Check all 6 scenes side by side. If any scene shows identity drift, regenerate it with the anchor scene as an additional reference. Minor inconsistencies in background or lighting are acceptable — face and body identity should be locked.

With Genra, this entire workflow collapses into a single conversation. Describe your 6-scene story, upload your character references, and the agent handles steps 3-6 automatically.

Pro Tips: Reference Images, Style Guides, and Prompt Techniques

Reference Image Best Practices

• Use 3-5 images minimum — front, three-quarter, and profile views
• 1024x1024 or higher resolution — low-res references produce low-confidence identity locks
• Consistent lighting — avoid mixing flash photos with natural light references
• Clean backgrounds — solid colors or blurred backgrounds help the model isolate character features
• Same outfit across references — clothing changes in references confuse identity systems

Style Guide Creation

A text-based style guide supplements visual references. Include:

• Physical description (hair, eyes, build, skin tone, age range)
• Clothing description (specific items, colors, style)
• Distinguishing features (scars, glasses, jewelry, tattoos)
• Mood and expression defaults (serious, cheerful, neutral)

Prompt Techniques for Consistency

• Name your character — Use a consistent name like "Maya" across all prompts. This creates an identity anchor in the model's attention.
• Repeat key features — Include "dark-haired woman in blue jacket" in every scene prompt, even if it feels redundant. Redundancy is your friend.
• Describe what doesn't change — "Same character as scene 1, same clothing, same hairstyle" explicitly tells the model what to preserve.
• Control the variables — Change one thing at a time between scenes. If the setting changes, keep the camera angle similar. If the angle changes, keep the lighting similar.

Common Mistakes That Break Consistency

• Changing too many variables at once — New setting + new angle + new lighting + new action = identity drift. Change one element per scene transition.
• Using low-quality reference images — Blurry, small, or poorly lit references give the model weak identity signals. Quality in, quality out.
• Ignoring clothing in prompts — Clothing is a major identity anchor. If you don't specify it, the model will improvise — and improvisation kills consistency.
• Not using an anchor scene — Generate your best character shot first and use it as reference for all subsequent scenes. Without an anchor, each scene drifts independently.
• Expecting perfection from one model — Different scenes demand different model strengths. A close-up dialogue scene and a wide action scene may need different models. Using a multi-model workflow through Genra gives you the best consistency across varied scene types.

Key Takeaways

• Character consistency is solvable in 2026 — it's moved from experimental to production-ready
• Four methods exist: start frame, keyframe interpolation, re-roll with modify, and separate compositing. Choose based on your project's complexity.
• No single model wins every scenario — Kling 3.0 leads multi-shot, Seedance 2.0 leads identity-lock, Sora 2 leads multi-character
• Reference images are the foundation — 3-5 images, 1024x1024+, clean backgrounds, consistent lighting
• A text style guide supplements visual references and prevents prompt drift
• Multi-model workflows deliver the best overall consistency — let Genra's agent handle the model selection automatically

Frequently Asked Questions

Why do AI video characters change appearance between scenes?

AI video models generate each shot independently by default, sampling from a probability distribution. Without explicit identity anchoring — like a reference image, keyframe, or identity-lock feature — the model has no memory of what the character looked like in previous scenes. Small variations compound across shots, resulting in noticeable identity drift.

Which AI video model has the best character consistency in 2026?

It depends on the scenario. For single-generation multi-shot, Kling 3.0's storyboard mode leads with up to 6 cuts. For identity-lock across sessions, Seedance 2.0 is strongest. For multi-character scenes, Sora 2 handles 5+ characters most reliably. A multi-model approach through Genra delivers the best overall results across varied project needs.

What resolution should reference images be for character consistency?

Use reference images of at least 1024x1024 pixels with the character clearly visible and well-lit. Include 3-5 images showing different angles and expressions. Clean or blurred backgrounds help the model isolate identity features more accurately.

Can I maintain character consistency across different AI video models?

Yes, with a consistent reference image pipeline. Use the same high-quality references across models and maintain a text style guide. Genra's agent automates this — it selects the best model for each scene type while maintaining character references throughout, ensuring consistency even when switching models.

How many scenes can I generate with consistent characters?

You can reliably maintain consistency across 6-12 scenes using reference images and model-specific features. Kling 3.0 handles up to 6 cuts natively. For longer sequences, break projects into 6-scene blocks using output from each block as reference anchors for the next. Genra's agent manages this automatically for any project length.