AI Character Design: From Concept to Canvas-Ready Asset

Indie game artists, animation studios, and solo creators all run into the same wall the first time they try to build a recurring character with AI tooling. The first hero render comes out striking. The second pass on a different angle drifts in identity. The wardrobe variant for a different scene loses the face. By the time the character needs to appear in motion, the look has wandered far enough from the original render that the pieces no longer feel like the same character. The structural cause is treating character design as a generation problem rather than a pipeline problem.

A canvas-ready character is the output of four stages working together. Concept exploration is where dozens of looks get sketched and one direction gets picked. Design lock is where the chosen look gets refined until it is the canonical version. Reference sheet construction is where the canonical look gets turned into a multi-angle, multi-expression library. Downstream usage is where the library feeds every shot, every wardrobe variant, every video appearance the character makes across the project. Skip any one stage and the character drifts.

On the Martini canvas, the four stages live as connected node clusters with shared references between them. The concept exploration nodes feed into the design lock nodes. The design lock nodes pin the canonical version. The reference sheet nodes wire from the canonical pin and produce the angle library. The downstream usage nodes — image and video both — wire the reference library in directly rather than chaining through previous outputs. This structural pattern is what turns AI character design from a brittle one-off render into a durable production asset.

Concept exploration is a breadth problem. The job at this stage is to look at thirty or fifty different versions of the character before committing to one direction. Midjourney is the strongest concept exploration node on the Martini canvas because of its aesthetic range, its visual variety per prompt, and the ease of pulling distinct stylistic directions from minor prompt variations. Drop a Midjourney node, write the character description in one or two sentences, and generate ten or fifteen takes per prompt. Iterate the prompt three or four times to widen the search.

Resist the instinct to fix on a direction in the first ten generations. Concept exploration earns its keep when it forces you to see directions you would not have written in the prompt. Save standout takes from each prompt iteration to the canvas reference panel; this becomes the moodboard of candidate directions. The discipline is to look at the full spread before picking, not to pick the first take that vaguely matches the brief.

Once the moodboard reads as five to eight distinct directions, narrow to two or three finalists by a stylistic-fit test. Which directions match the world the character lives in? Which match the genre, the tone, the production constraint of the project? The finalists are not necessarily the prettiest renders — they are the ones that fit the project context. Pin the finalists in the canvas as the candidate canonical looks for the design lock stage.

Design lock is a depth problem. The job at this stage is to take the finalist direction from concept exploration and refine it into the canonical version that every downstream asset will reference. Nano Banana 2 is the right node for design lock because of its consistency under prompt variation, its handling of detailed character description, and its responsiveness to reference image input. Drop a Nano Banana 2 node, write the detailed character description (build, face, age range, hair, eye color, signature wardrobe, mood), and feed in the chosen Midjourney finalist as the reference image.

Generate four to six takes of the front view. Compare them side by side. Pick the one that best reads as the character — the one that captures the personality, the build, the visual signatures all at once. Pin this as the canonical front view. This pinned image is the contractual source of truth for everything downstream; no further generation in the pipeline should drift from this canonical pin.

For characters that need design refinement at this stage — adjusting a feature, dialing in the exact wardrobe, fixing a proportion — drop a Flux Kontext node downstream of the canonical pin and run surgical edits. Flux Kontext respects the source identity and applies the requested change without redrawing the character from scratch. Re-pin the refined version as the new canonical front view. Iterate up to two or three rounds of refinement; more than that usually signals the concept exploration was not deep enough and the right call is to step back rather than keep editing.

The reference sheet is the canonical multi-angle library that downstream nodes will pull from. Drop four Nano Banana 2 nodes downstream of the canonical front view, each wired to the front view as a reference image. Prompt each for a different angle: three-quarter left, three-quarter right, profile (left side facing right), and a full-body shot at the same angle as the front view. Generate three takes per node, pick the strongest from each version tray, and pin them as the canonical angle library.

Add an expression set. Drop another four to six Nano Banana 2 nodes wired to the canonical front view. Prompt each for a specific expression beat — neutral, smiling, focused, surprised, exhausted, determined. The expression set is what unlocks emotional storytelling in downstream shots; without it, the character can only appear in one mood. Pin the expression takes alongside the angle library. The combined sheet now has roughly ten to twelve canonical references.

For a project with significant volume — a season of game cinematics, a multi-episode animated series, a character that appears in dozens of shots — extend the reference sheet to include wardrobe variants, signature poses, and any recurring props the character carries. Each addition is one Nano Banana 2 node wired to the canonical front view with a prompt that varies one element. The reference sheet becomes a living document that grows as the project demands more from the character.

Once the reference sheet is built, image-side downstream work becomes a wired-in operation. For a new scene image, drop a Nano Banana 2 node, wire in three or four references from the canonical sheet (typically the front view, the matching angle, and the relevant expression), and prompt the new scene context. The character looks like the same person because the canonical references are the source of truth on every generation rather than chained through previous outputs.

For wardrobe-driven projects (a fashion-forward character, a multi-outfit cast member, a character that needs costume variants for different chapters), Flux Kontext is the production workhorse. Pin a canonical pose from the reference sheet, drop a Flux Kontext node downstream, and generate fifteen wardrobe variants from that one pose. The face stays. The pose stays. Only the wardrobe changes. This is dramatically faster than re-generating from scratch and produces tighter visual consistency across the wardrobe set.

For character interactions — two characters in the same frame — wire references from both characters' canonical sheets into the same Nano Banana 2 node. The model handles two-character scenes more reliably when both characters have full reference libraries to pull from. Lock the canonical interaction once and reference it as a new pinned asset for downstream variants of the same scene.

Video adds a temporal dimension to the consistency challenge — the character has to look like the same person across frames within a shot, and across shots within a sequence. Vidu Q2 Subject Reference is the strongest video node on Martini for character-first work because it accepts up to seven reference images directly. Wire the front view, both three-quarter angles, the profile, and two expression takes into a Vidu Q2 node, write the motion prompt for the shot, and render. The character identity holds across the take because the model has the full angle library to interpolate from.

Kling O3 Reference is the right node when the shot needs the character interacting with a specific scene or prop. Wire the canonical references and a scene reference together; Kling O3 handles the character-plus-scene pairing more cleanly than character-only video models. Use this for scenes where the relationship between the character and their environment is the visual point — a character at a workbench, a character facing a vista, a character with a signature object.

Seedance 2 Omni is the cinematic slot. Wire the canonical front view and one matching angle into a Seedance 2 Omni node, write the shot prompt as a single take with subject, action, camera move, and lighting. Seedance handles cinematic motion realism with the strongest fidelity to the source character. For a multi-shot sequence — wide, medium, close-up of the same character — duplicate the Seedance node across the sequence and vary only the prompt; the canonical references stay wired into every duplicate.

The character library is most valuable when it is treated as a project-long asset rather than a one-shot effort. As the project develops, certain looks become signature for the character — a wardrobe choice that read well in early shots, an expression that captured a defining mood, a pose that audiences associated with the character. Add those to the canonical sheet. Retire references that no longer represent the character's evolved look. The library evolves; the truth at any given moment is the currently-pinned set.

For collaborators on the project — other artists, animators, NLE editors, marketing partners — the canonical library is also the contractual handoff. Export the pinned references as a sharable set. Anyone working on a new asset references the same library, which means the character looks like the same person across every artist's contribution. This is the production-team equivalent of the consistency property the canvas gives a single artist.

For long-running projects (a multi-season game, a multi-year animated series), version the library at major project milestones. The character at season one and the character at season three may legitimately have evolved looks; the library should reflect that evolution rather than pretend nothing has changed. Pin a season-one library as the source of truth for season-one shots, a season-three library for season-three shots, and a transitional set for any flashback or continuity work that bridges the two.

Outside a canvas-based tool, AI character design is a multi-tool sequence — generate concepts in one product, download finalists, switch to another tool for refinement, download the canonical version, switch to another tool for video, upload references one shot at a time, generate, download, switch to an editor to assemble. Each transition silently introduces identity drift because the references rarely make it cleanly across tool boundaries. Indie game artists who have tried to build a recurring AI character without a canvas usually plateau because the per-shot overhead caps how much they can ship before consistency breaks.

On the Martini canvas, the entire chain — concept, design lock, reference sheet, image downstream, video downstream — lives in one workspace with pinned references that every downstream node wires into directly. The version tray remembers every take from every stage so the canonical pin is one click away even after hundreds of downstream generations. The reference investment compounds across the entire project rather than degrading at every tool boundary. A character designed once on the canvas can credibly carry a season of game cinematics, an episodic animated series, or a multi-chapter visual novel without identity drift.