Character Consistency with Elements: Maintain Identities Across Kling 3.0 Generations

What is the Elements System?

The Elements system is Kling 3.0's built-in feature for maintaining visual consistency of characters, faces, and objects across multiple video generations. In standard AI video generation, each new prompt produces entirely new characters and objects, making it impossible to create a series of videos featuring the same person, mascot, or branded item. Elements solves this problem by allowing you to upload reference images that the AI uses as a visual anchor, ensuring that the generated character or object matches your reference in every new video.

Think of Elements as casting an actor for your AI-generated production. Just as a film director casts a specific actor who appears consistently throughout a movie, Elements lets you "cast" a specific face, body type, or object design that Kling 3.0 will faithfully reproduce regardless of the scene, action, or environment described in your prompt. The system extracts identifying visual features from your reference images -- facial structure, skin tone, hair style, clothing patterns, object shape, color palette -- and embeds them into the generation process as persistent constraints.

Before Elements, creators who needed character consistency had to resort to elaborate workarounds: generating hundreds of variations and manually selecting the ones that happened to look similar, or using external face-swapping tools in post-production. These approaches were time-consuming, inconsistent, and often produced uncanny results. Kling 3.0's Elements system integrates consistency directly into the generation pipeline, producing results that are both more reliable and more natural-looking than post-processing alternatives.

The Elements system supports multiple simultaneous references, allowing you to maintain consistency for up to four distinct characters or objects within a single generation. This makes it possible to create scenes with multiple recurring characters, each preserving their unique visual identity. Whether you are building a narrative series, a brand campaign, or an educational content library, Elements gives you the consistency that transforms isolated clips into cohesive visual storytelling.

How Elements Work

Under the hood, Kling 3.0's Elements system operates through a multi-stage process that begins the moment you upload your reference images. In the first stage, the system performs feature extraction, analyzing each reference image to identify and encode the defining visual characteristics of the subject. For faces, this includes facial geometry (the relative positions and proportions of eyes, nose, mouth, jawline), skin texture, hair characteristics, and distinctive features such as freckles, scars, or facial hair. For objects, the system captures shape silhouettes, color distribution, surface textures, and proportional relationships between component parts.

When you upload multiple reference images of the same subject, the system performs feature consolidation. It cross-references the visual information across all provided references to build a more robust and complete identity model. A single frontal photograph provides limited information about how a person looks from different angles, but adding a profile view and a three-quarter view allows the system to construct a more three-dimensional understanding of the subject's appearance. This is why providing multiple angles dramatically improves consistency -- the AI has more information to work with when generating the subject in novel poses and perspectives.

During video generation, the extracted features are injected into the diffusion process as conditioning signals. These signals guide the denoising steps to produce frames where the specified character or object matches the reference identity. The system balances two competing objectives: faithfulness to the reference identity and responsiveness to the text prompt. If your prompt describes the character running through rain, the system must render the character's face accurately while also showing motion, wet hair, splashing water, and appropriate lighting -- all in a way that looks natural and coherent.

The quality of this balance depends heavily on the quality and clarity of your reference images, the specificity of your prompt, and the degree of visual change demanded by the scene. Simple, well-lit reference images combined with prompts that do not dramatically alter the character's appearance (for example, changing hair color or adding large accessories not present in the reference) produce the most consistent results. We will explore how to optimize each of these variables in the following sections.

Step 1: Preparing Reference Images

The foundation of successful character consistency is high-quality reference images. The reference images you provide are the only visual information the AI has about your character's identity, so every detail matters. Start by selecting photographs that are clear and well-lit. The subject's face (or the object's key features) should be clearly visible without obscuring shadows, heavy backlighting, or motion blur. Studio-quality portraits with even, diffused lighting produce the best results, but well-exposed natural light photographs work well too. Avoid images where sunglasses, hats, scarves, or other accessories obscure significant portions of the face.

Providing multiple angles significantly improves consistency. At minimum, include one frontal view where the subject is looking directly at the camera. Ideally, supplement this with a three-quarter view (the subject turned roughly 45 degrees) and a profile or near-profile view. These additional angles give the AI a more complete understanding of the subject's three-dimensional facial structure, enabling it to generate the character convincingly when the prompt calls for different head positions, camera angles, or action poses. Three well-chosen reference images from different angles consistently outperform a single high-quality image.

Maintain consistent lighting across your reference images. If one reference is shot in warm tungsten light and another in cool daylight, the AI may struggle to reconcile the differing color temperatures and produce inconsistent skin tones. Ideally, photograph all your references in the same session under the same lighting conditions. If that is not possible, at least ensure that the lighting temperature and direction are similar across references. Post-processing your references to match white balance and exposure levels can help if the original lighting conditions varied.

Keep the subject's appearance consistent across references. If your character wears glasses in one reference but not in another, the AI may inconsistently include or exclude the glasses in generated videos. If your character has a specific hairstyle, all references should show that hairstyle. Slight natural variations (a small head tilt, a different facial expression) are fine and even helpful, but large appearance changes between references introduce ambiguity that degrades consistency. Think of your references as defining a canonical version of the character -- every reference should represent the same "version."

Step 2: Setting Up Elements

To access the Elements system, navigate to the Kling 3.0 creation interface and select the Elements mode or toggle. Depending on the interface version, this may appear as a dedicated tab, a sidebar panel, or an expandable section within the standard generation view. Once activated, you will see upload slots for reference images along with configuration options for how those references should be used during generation.

Upload your prepared reference images by clicking the upload slots or dragging and dropping image files. Kling 3.0 allows you to upload up to 4 reference images per generation. You can use all four slots for a single character (providing different angles of the same person for maximum consistency), or you can distribute them across multiple characters or objects. For example, you might use two slots for a main character, one slot for a secondary character, and one slot for a branded object that should appear in the scene.

After uploading, assign roles to each reference image. The role assignment tells the AI how to interpret and prioritize each reference during generation. The available roles typically include:

• Main Character: The primary subject of the video. This reference receives the highest consistency priority and is typically the most prominent figure in the generated output.
• Secondary Character: A supporting character who appears alongside the main character. The AI will maintain their identity but may give them slightly less screen prominence.
• Object: A non-human element such as a product, prop, vehicle, or branded item that should maintain its visual identity throughout the video.

Role assignment matters because it affects how the AI allocates its consistency budget. When multiple elements compete for visual attention in a single frame, the AI prioritizes the main character's identity fidelity over secondary characters and objects. If you find that a secondary character is losing consistency while the main character looks perfect, try reducing the visual complexity of the scene or generating separate clips for each character.

Step 3: Writing Prompts with Elements

Writing prompts that work well with Elements requires a different approach than standard text-to-video prompting. When Elements are active, your prompt needs to explicitly reference the named elements you have set up so the AI knows which character or object to render with which identity. The key principle is to use the exact name you assigned to each element when describing their actions, positions, and interactions in the scene.

The prompt structure for Elements-based generation typically follows the pattern: [Element Name] performs [action] in [setting], [style/mood modifiers]. The element name acts as a trigger that tells the AI to pull the visual identity from the corresponding reference images rather than generating a random character. Without the explicit name reference, the AI may generate a generic character that does not match your uploaded references.

Here are three tested prompt examples that demonstrate effective Elements prompting:

When crafting your prompts, avoid describing physical attributes that contradict your reference images. If your reference for "Sarah" shows a woman with short brown hair, do not prompt for "Sarah with long blonde hair" -- this creates a conflict between the reference identity and the prompt description, and the results will be unpredictable. Instead, let the reference images handle appearance and use the prompt to direct action, setting, mood, and camera work.

Step 4: Fine-tuning Results

After your first generation with Elements, you will likely want to fine-tune the results to achieve better consistency or visual quality. The most impactful adjustment is prompt weight, which controls the balance between reference identity fidelity and prompt responsiveness. Higher reference weight produces stronger resemblance to your uploaded images but may reduce the AI's ability to place the character in dramatically different poses or settings. Lower reference weight gives the AI more creative freedom but may allow the character's appearance to drift from the reference. Start at the default weight and adjust in small increments based on your results.

Choosing the best reference angles for a given scene can dramatically improve consistency. If your prompt describes a character in profile view, having a profile reference image will produce much better results than relying solely on a frontal reference. Review your generated output and identify which angles or poses show the weakest consistency. Then, either add a reference image from that specific angle or adjust your prompt to favor angles that your existing references cover well. Over time, you will develop an intuition for which reference combinations work best for different types of scenes.

Iteration is essential. Treat your first generation as a draft and use it to identify what is working and what needs adjustment. Common issues to watch for include: the character's face looking correct but their body proportions changing, the character's identity being strong in some frames but drifting in others (temporal inconsistency), or the character looking right but the scene composition being wrong. Each of these issues has a different fix -- body proportion issues often respond to better reference images, temporal inconsistency improves with higher reference weight, and composition issues are best addressed through prompt refinement.

If you are creating a series of videos with the same character, establish a reference set early in your workflow and reuse it across all generations. Changing references mid-series -- even if the new references show the same person -- can introduce subtle visual shifts that break the sense of continuity between clips. Treat your reference set as locked once you begin production, and only update it if absolutely necessary.

Use Cases

Brand mascot videos. Companies with established mascot characters can use Elements to generate video content featuring their mascot in any scenario, setting, or narrative without commissioning traditional animation or 3D rendering for each new piece of content. Upload reference images of the mascot from the brand style guide, and the AI will maintain its visual identity across product launches, seasonal campaigns, social media posts, and internal communications. This is particularly valuable for small businesses and startups that have a mascot design but lack the budget for professional animation.

Consistent character series. Content creators building episodic content -- whether short-form social media series, educational character-driven explanations, or narrative entertainment -- need their characters to look the same from episode to episode. Elements enables this by preserving facial features, body type, and general appearance across independent generations. A YouTube creator can produce a weekly series featuring their AI character, and viewers will recognize the character instantly in each new installment. This consistency is what transforms random AI clips into an actual show with recurring characters that audiences connect with.

Product line videos. E-commerce brands with multiple products in the same line can use object Elements to maintain consistent product representation across marketing videos. Upload reference images of each product, and the AI will accurately render the product's colors, logos, proportions, and design details in diverse settings and use cases. A cosmetics brand, for example, could generate videos showing the same lipstick shade in different lighting conditions, on different backgrounds, and in various creative compositions, all while maintaining exact color consistency with the actual product.

Storytelling and narrative content. Perhaps the most exciting use case for Elements is multi-scene storytelling. By defining characters as Elements, creators can generate individual scenes that star the same cast and then edit them together into a cohesive narrative. A character introduced in scene one walking down a street will be recognizable when they appear in scene five sitting in a restaurant. This capability bridges the gap between AI video generation and traditional filmmaking, where character consistency is a given rather than a challenge. Indie filmmakers and animators are already using Elements-based workflows to create short films and pilot episodes that would otherwise require significant casting and production resources.

Limitations and Workarounds

Maximum of 4 reference images. The current Elements system limits you to 4 reference image uploads per generation. For a single character, this typically means 3-4 angles of the same person, which is usually sufficient for strong consistency. However, when you need multiple characters in one scene, you must divide the 4 available slots among them. With two characters at two references each, consistency for both individuals is good. With four characters at one reference each, consistency may be weaker since the AI has less information per character. The workaround for scenes requiring many characters is to generate the scene multiple times, each time focusing on a different pair of characters, and then composite the best results in post-production.

Style consistency across different environments. While Elements excels at preserving facial and object identity, it does not guarantee consistent stylistic treatment across different scenes. A character generated in a sunny outdoor scene may have a slightly different visual style than the same character in a dark indoor scene because the AI adapts its rendering to match the environment described in the prompt. To maintain stylistic consistency, include the same style modifiers in every prompt of your series: if your first video uses "cinematic, soft lighting, film grain, warm color palette," use those same modifiers in all subsequent prompts. This trains the AI to apply a uniform aesthetic treatment regardless of the scene content.

Limitations with extreme poses and angles. Elements-based consistency works best when the generated character appears at angles reasonably close to one of the reference images. If your references only show frontal views but your prompt describes the character from directly above or from behind, the AI must extrapolate the character's appearance at an angle it has never seen, which can lead to identity drift. The workaround is straightforward: anticipate the angles you will need in your final videos and include reference images from those angles. If you plan a scene where the character turns away from camera, capture a back-view reference.

Temporal consistency within longer clips. Even with strong reference images, characters in 10-second generations may show subtle identity drift between the beginning and end of the clip, particularly during rapid motion or camera changes. The character's face may shift slightly in proportions or lighting response over the duration. For critical projects, generating shorter 5-second clips with high reference weight and then editing them together produces more consistent results than relying on a single long generation. The edit points can be masked with transition effects that feel intentional rather than compensatory.

Tips for Best Results

Build a reference image library over time. If you work with recurring characters regularly, invest time upfront in creating a comprehensive reference set. Photograph your subject (or render your 3D character) from 8-12 different angles in consistent lighting, and save these as your master reference library. For any given generation, you can then select the 4 most relevant angles from your library based on the specific scene you are creating. This approach gives you maximum flexibility while maintaining rock-solid consistency.

Test with simple scenes first. Before committing to a complex multi-character scene with elaborate action, test your elements with simple prompts: "Sarah stands in a neutral studio, facing the camera, soft lighting." This baseline test lets you verify that the identity extraction is working correctly before introducing variables like motion, secondary characters, or complex environments. If the character does not look right in a simple test, no amount of prompt engineering will fix it in a complex scene -- you need to improve your reference images.

Document what works. Keep a log of your successful generations, including the reference images used, the exact prompts, and the settings. When you find a combination that produces excellent consistency, you can replicate it reliably for future projects. Over time, this documentation becomes an invaluable personal knowledge base that dramatically accelerates your workflow. Pay particular attention to which reference image combinations produce the strongest identity preservation -- you may discover that certain angles or lighting conditions work especially well for your specific characters.