perfectpixel

PerfectPixel

An AI-powered animation sprite generation studio

Turn a one-line character description into a base character, then auto-generate 100+ motion animations (walk, run, attack, magic, …) and complete 8-direction sprite sets — exported in formats your game engine can import directly.

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_{A power-up animation generated end-to-end by the actual pipeline (gemini-3-pro-image).}

Why this is a hard problem

Plenty of tools draw a pretty picture. Game developers need something else: a 6-frame walk must be exactly 6 frames, every frame must read as the same character, the background must be fully alpha-transparent, and the character's anchor must not jitter between frames — all in a format an engine can import as-is.

AI models are bad at these constraints. Frame counts come out wrong, poses bleed into each other, hair color drifts mid-strip (identity drift), and the chroma-key background spills onto the character's silhouette.

PerfectPixel's core idea: let the AI render, but enforce quality, consistency, and precision with a deterministic post-processing pipeline. Even when the AI's output is non-deterministic, the result converges to a consistent quality bar. That is the engineering moat — see the before/after comparisons below, all produced by the real pipeline code on real AI output.

Features

• Text → character: describe a character, pick a style (pixel art, chibi, cartoon …), and generate a base character with the background auto-removed.
• 100+ motion presets: a categorized keyword catalog — walk, run, jump, attack, magic, emotes, and more.
• 8-direction sets: 5 directions are generated by AI and the 3 mirror directions are derived by horizontal mirroring, for a consistent set at 37.5% lower generation cost.
• Self-correcting quality loop: generate → matte → extract → inspect → corrective regenerate, up to 3 passes, converging frame count and motion quality.
• Real pixelization: shared-palette quantization + pixel-grid snapping for authentic dot art.
• Engine-friendly export: sprite sheet + manifest.json + Aseprite-compatible JSON + per-state GIF/APNG + individual frame PNGs, all in one pass.
• Multi-provider: choose Gemini, OpenRouter, fal.ai, or BytePlus as the backend.
• Sessions & gallery: work state is persisted to disk and results are auto-archived.

Sample Output

Walk	Idle	Cheer	Power Up
Dance	Victory	Block	Low HP

How it works

Generating one state (animation) runs this pipeline in the Go backend:

description + style + motion preset
        │
        ▼
  build prompt ──► AI image generation (horizontal filmstrip)
        │
        ▼
  background detect & matte ──► frame extraction (cell segmentation)
        │
        ▼
  inspect (frame count / identity drift / motion presence)
        │
        ├─ pass ─────────────► pixel quantization ──► done
        │
        └─ fail ─► corrective retry hint ──► regenerate (up to 3×)

Two things make this more than a naive retry loop:

• Best-candidate scoring — every attempt is scored score = Found*100 − errors*10, and the best candidate is kept. A perfect result returns immediately; if 3 passes still aren't perfect, the best-so-far is returned (never an empty hand). API errors and cancellations bail early.
• Measurement-driven retry hints — defects detected during inspection are converted into precise English correction instructions injected into the next prompt (e.g. "the previous result read as 7 poses but exactly 6 are required; split the canvas into 6 even columns…"). Combined with user feedback, each pass becomes a closed-loop self-correction that converges on the defect rather than rolling the dice again.

The engineering: deterministic correction

The design philosophy in one line: signal processing, not heuristics. Three signal-processing axes — wrapped in a self-diagnostic, self-correcting closed loop.