Character Consistency in AI Video: How to Keep Characters Looking the Same

Character consistency is the single hardest problem in AI video production today. Every creator who has tried to build a multi-shot narrative with AI-generated footage has faced the same frustrating result: the character in shot one looks completely different from the character in shot two. Hair color shifts, facial features morph, clothing changes, and the overall identity of the character drifts from clip to clip.

The good news is that this problem is solvable with current tools and techniques. This guide covers four proven methods to maintain character consistency, explains when to use each one, and provides a practical workflow that combines them for the best results. Whether you are creating a short film, an explainer series, or a product video with a recurring presenter, these methods will help you maintain a consistent character across every shot.

Why Character Consistency Is Hard

AI video generators create each frame and each clip as an independent sampling process from a learned distribution. When you type a prompt describing a character, the model does not remember what that character looked like in a previous generation. It creates a new interpretation every time, pulling from the vast space of possible visual outputs that match your text description.

This is fundamentally different from traditional filmmaking, where you have a real actor who looks the same across every take. In AI video, there is no persistent identity. The model has no concept of "the same person" between two separate generation calls. Even if you use identical prompts, the stochastic nature of the diffusion process means the output will vary. Small differences in the random seed, the denoising path, or the latent space sampling all compound into visible changes in the final character appearance.

This makes character consistency the number one pain point for AI filmmakers trying to create anything beyond single-shot content. Short films, product videos with recurring presenters, explainer series, and narrative content all require solving this problem before they can be produced at a professional level.

Method 1: Image-to-Video with Reference

The most reliable method for character consistency available today is Image-to-Video (I2V) generation. Instead of describing your character with text, you provide the model with an actual image of the character and ask it to animate that image. Since the model starts from a fixed visual reference, the output maintains strong consistency with the source.

This approach works because the model uses the pixel data from your reference image as the starting point for the diffusion process, rather than generating appearance from scratch based on text. The character's face, clothing, and body proportions are all anchored to real pixel values from the first frame onward.

How It Works

1. Create a reference image of your character using an AI image generator (Midjourney, DALL-E, Flux) or a real photograph
2. Upload the reference image to the I2V interface of your chosen video generator
3. Write a motion-focused prompt that describes how the character should move, not what they look like (the model can already see that from the image)
4. Generate the video and review for consistency

Best Practices for Reference Images

Your reference image quality directly impacts the consistency of your output:

• Use high resolution images (1024px or higher on the longest side)
• Ensure the character has clear separation from the background
• Choose a neutral pose that allows for natural animation
• Maintain consistent lighting without extreme shadows or highlights
• If using AI-generated images, save the seed and prompt for reproducing similar references

Supported Tools

Pros and Cons

Pros:

• Highest consistency of any method
• Easy to set up with no training required
• Works across most modern AI video generators
• Results are immediately usable

Cons:

• Character is locked to the starting pose and framing of the reference image
• Difficult to generate wide variation in camera angles from a single reference
• Each new shot requires careful selection of the starting reference image
• The character may diverge from the reference during longer clips or complex motion

Method 2: LoRA Training

LoRA (Low-Rank Adaptation) training creates a small model adapter that encodes the visual identity of your character. Once trained, this adapter can be applied to any generation, allowing the model to produce your specific character in any pose, scene, or lighting condition while maintaining identity.

Think of a LoRA as teaching the model a new concept. Instead of relying on the model's general understanding of what a person might look like, you give it a specific visual vocabulary for your character. The adapter file is typically small (50-200 MB) and can be shared, reused, and combined with other LoRAs.

How It Works

1. Collect 10-20 high-quality images of your character from various angles and in different lighting conditions
2. Prepare the training dataset by captioning each image with a trigger word (e.g., "ohwx person") and a description
3. Run LoRA training on a platform like Replicate, Civitai, or locally using ComfyUI with the kohya trainer
4. Apply the LoRA during generation by referencing the trigger word in your prompt

Training Data Requirements

When to Use LoRA

LoRA training is most valuable when you need a recurring character across many videos. The upfront cost in time and compute is justified when the character will appear in dozens or hundreds of clips. For a one-off video with a few shots, I2V with a reference image is more practical.

Platforms for LoRA Training

• Replicate: Cloud-based training, pay per compute minute, no local setup required
• Civitai: Community platform with training tools and shared LoRA models
• ComfyUI + kohya: Local training for maximum control, requires a GPU with 12GB+ VRAM
• RunPod: Rent cloud GPUs for local-style training at lower cost

Pros and Cons

Pros:

• Works across many poses, scenes, and lighting conditions
• Once trained, can be reused indefinitely
• Produces the most flexible character consistency
• Can be combined with other methods for even stronger results

Cons:

• Requires collecting or generating a training dataset
• Training takes time (30 minutes to several hours depending on platform)
• Costs money for compute or platform fees
• Technical setup can be challenging for beginners
• LoRA quality depends heavily on training data quality

Method 3: Multi-Shot Prompt Anchoring

Prompt anchoring is a pure prompt engineering technique that requires no additional tools, training, or setup. The core idea is to include an identical, detailed character description in every prompt you write, creating a textual anchor that constrains the model to generate similar-looking characters across shots.

While less precise than visual reference methods, prompt anchoring is the most accessible technique and works with every text-to-video generator on the market. It is often the first method creators try, and for characters with bold, distinctive features (bright clothing, unusual hair color, distinctive accessories), it can produce surprisingly good results.

How It Works

1. Write a detailed character description with specific, measurable attributes
2. Copy this exact description into every prompt that features this character
3. Keep all other prompt elements consistent (style, lighting, color grading)
4. Vary only the action and camera angle between shots

Writing an Effective Character Anchor

The key is specificity. Vague descriptions produce vague consistency. Strong anchors include:

Weak anchor (too vague):

A young woman with dark hair

Strong anchor (specific and measurable):

A 30-year-old East Asian woman with shoulder-length straight black hair,
brown eyes, light skin, wearing a fitted red leather jacket over a white
crew-neck t-shirt, dark blue slim jeans, white sneakers

Tips for Stronger Anchoring

• Include age, ethnicity, hair length/color/style, eye color, and skin tone
• Describe clothing in detail including color, material, and fit
• Mention accessories (glasses, watch, necklace) consistently
• Specify body type and height relative to the frame
• Use the same descriptive words in the same order across all prompts
• Add a visual style anchor as well (e.g., "cinematic, shot on 35mm, teal and orange grading")

Example Multi-Shot Sequence

Shot 1 (wide establishing):

Wide shot of a 30-year-old woman with shoulder-length black hair wearing
a red jacket and white t-shirt, walking through a busy city market at
golden hour, cinematic lighting, slow tracking shot

Shot 2 (medium close-up):

Medium close-up of a 30-year-old woman with shoulder-length black hair
wearing a red jacket and white t-shirt, examining fruit at a market stall,
warm natural lighting, shallow depth of field, static camera

Shot 3 (over the shoulder):

Over-the-shoulder shot of a 30-year-old woman with shoulder-length black
hair wearing a red jacket and white t-shirt, paying a vendor at an outdoor
market, golden hour backlight, slight camera push-in

Pros and Cons

Pros:

• No setup, training, or additional tools required
• Works with every text-to-video generator
• Free to use
• Quick to implement

Cons:

• Less precise than I2V or LoRA methods
• Works better for simple, distinctive character designs
• Subtle features (specific face shape, exact proportions) are unreliable
• Consistency degrades with complex characters or varied camera angles

Method 4: Post-Production Face Swap

Face swapping applies a consistent face to AI-generated video as a post-processing step. You generate the video with any face, then replace it with your target face using specialized tools. This decouples the face identity from the video generation process entirely.

This method treats character consistency as a post-production problem rather than a generation problem. The advantage is that you can focus on getting the best motion, composition, and lighting during generation without worrying about facial identity. The identity is applied afterward as a separate step.

How It Works

1. Generate your video using any method (text-to-video, image-to-video)
2. Prepare a reference face image of the character you want (clear, front-facing, well-lit)
3. Run the face swap tool on the generated video, providing the reference face
4. Review and refine the output for natural blending

Tools for Face Swapping

When to Use Face Swap

Face swapping is best used as a cleanup step when other methods produce near-consistent results but with minor face variations. It is less ideal as a primary strategy because it can create unnatural blending artifacts, especially with extreme head angles, strong lighting, or fast motion.

The ideal workflow is to generate your video using I2V or prompt anchoring first, then apply face swap only to the clips where the face has drifted noticeably. This targeted approach minimizes artifacts while maximizing consistency across the final edit.

Pros and Cons

Pros:

• Works with any video source regardless of generation method
• Produces pixel-exact face consistency when conditions are favorable
• Can fix consistency issues after the fact
• Open source tools available at no cost

Cons:

• Can look unnatural in challenging lighting or angles
• Raises ethical concerns around deepfake technology
• May violate platform terms of service
• Requires additional processing time per video
• Results degrade with low resolution source material

Tool Comparison for Consistency

Choosing the right tool matters because each platform has different strengths when it comes to maintaining character consistency. The following table summarizes how current AI video generators perform across the four consistency methods:

The best tool depends on your primary method. If you rely on I2V, Seedance 2.0 and Runway Gen-4 produce the strongest results. If you need LoRA flexibility, ComfyUI with local training gives you the most control. For quick projects where prompt anchoring is sufficient, any tool with good prompt understanding will work.

Step-by-Step Workflow

No single method solves character consistency perfectly in every situation. The most effective approach combines multiple methods at different stages of production. Here is a complete workflow that combines all four methods for maximum character consistency across a multi-shot video project.

Step 1: Create a Character Sheet

Use an AI image generator (Midjourney, DALL-E 3, or Flux) to create a character reference sheet. Generate 4-6 images of your character from different angles with consistent features. Save the best images and note the prompts and seeds used.

A good character sheet includes: one front-facing headshot, one three-quarter angle portrait, one full-body shot, and one or two action poses. Keep the lighting and style consistent across all images. If using Midjourney, lock the style seed and vary only the camera angle and pose between generations.

Step 2: Select the Hero Reference Image

Choose the single best image from your character sheet. This will be the primary reference for I2V generation. Pick an image with:

• Clear, well-lit face
• Neutral or natural expression
• Full view of clothing and accessories
• Clean background separation

Step 3: Generate Hero Shots with I2V

Use the hero reference image as input for your most important shots. These are typically close-ups and medium shots where character recognition is critical. Write motion-focused prompts and generate through your preferred I2V tool.

For each hero shot, focus your prompt entirely on motion and camera movement. Do not re-describe the character's appearance since the model already has the visual reference. Instead, write prompts like "The subject turns head slowly to the right and smiles, soft breeze moving hair, slow push-in toward face" rather than describing what the person looks like.

Step 4: Generate Supporting Shots with Prompt Anchoring

For wide shots, cutaways, and angles where the face is less prominent, use text-to-video with a strong character anchor prompt. Match the visual style, color grading, and lighting descriptions from your I2V shots to maintain overall consistency.

This is where prompt anchoring shines. In wide shots and cutaways, the face occupies fewer pixels and viewers are less sensitive to subtle facial differences. A strong clothing and body description anchor is often sufficient to maintain the illusion of the same character across these supplementary shots.

Step 5: Apply Face Swap for Cleanup

Review all generated clips side by side with your reference image. Identify any shots where the face has drifted noticeably from your reference. Apply face swap using InsightFace or FaceFusion to bring those shots back into alignment. Focus on clips where the character's face is clearly visible and the inconsistency would be obvious to viewers.

Step 6: Color Grade for Visual Consistency

Even with consistent characters, different generation calls can produce slightly different color temperatures and contrast levels. Import all clips into a video editor (DaVinci Resolve, CapCut) and apply a unified color grade to tie everything together visually.

Start by matching the exposure and white balance across all clips. Then apply a single creative LUT or color grade to the entire timeline. This creates the impression of a single continuous shoot rather than a collection of independently generated clips. Pay special attention to skin tones, as even small color shifts in skin can break the illusion of character consistency.

Step 7: Final Review

Watch the assembled sequence from start to finish without stopping. Your first impression as a viewer matters. Then watch a second time and check for:

• Face consistency across all shots
• Clothing and accessory consistency
• Hair style and color consistency
• Overall visual style coherence
• Smooth transitions between shots
• Skin tone uniformity across different lighting setups
• Proportional consistency (character height, build)

If any issues stand out, return to the relevant step and regenerate or reprocess the problematic clips. The goal is for a viewer to watch the final video without noticing that it was assembled from separately generated clips.

FAQ

Below are the most common questions creators ask about maintaining character consistency in AI-generated video.

Can AI keep the same character across multiple videos?

Not automatically. AI video generators do not have persistent memory of characters between generation calls. You need to use one or more of the methods described in this guide (I2V reference, LoRA training, prompt anchoring, or face swap) to maintain consistency manually.

What is the best tool for character consistency?

For most creators, Image-to-Video generation with a strong reference image is the most accessible and reliable method. Seedance 2.0 and Runway Gen-4 offer the best I2V quality. For advanced users who need maximum flexibility, LoRA training through ComfyUI provides the strongest results across varied scenes.

Do I need to train a LoRA for every character?

Yes, each character requires its own LoRA adapter trained on images of that specific character. However, once trained, a LoRA can be reused across unlimited generations. The investment pays off when a character appears in many videos.

How many reference images do I need?

For I2V generation, you need just one high-quality reference image per shot. For LoRA training, you need 10-20 images minimum, with 20-30 images producing the best results. These images should show the character from various angles and in different lighting.

Does Seedance support character consistency?

Seedance 2.0 supports character consistency primarily through its Image-to-Video mode. Upload a reference image of your character and write a motion-focused prompt. The model will animate the reference while preserving the character's appearance. For more on Seedance capabilities, see our Seedance 2.0 tutorial.

Is face swapping ethical in AI video?

Face swapping is a powerful tool that carries significant ethical responsibilities. Using it on your own original AI-generated characters is generally acceptable since no real person is involved. Using real faces with the person's explicit consent for creative projects is also considered ethical practice. However, using it to impersonate real people without consent is unethical and potentially illegal in many jurisdictions. Many platforms explicitly prohibit deepfake content in their terms of service. Always disclose AI-generated content and obtain written consent when using real likenesses.

Will character consistency improve in 2026?

Significantly. Multiple AI labs are actively working on persistent character identity as a core model feature. Kling has already introduced character-specific generation modes, and other platforms are expected to follow. By late 2026, built-in character consistency is likely to be a standard feature in major AI video generators, reducing the need for the manual methods described in this guide. In the meantime, the methods in this article represent the best available approaches for maintaining consistent characters today.

Related Articles

• Seedance 2.0 Tutorial -- Complete guide to getting started with Seedance
• Seedance Prompt Guide -- Master prompt writing for AI video generation
• Seedance vs Kling -- Compare the top AI video generators
• Seedance vs Sora 2026 -- Head-to-head comparison of leading models