Automated Multimodal Vision Audits: Grading Character Consistency Frame-by-Frame

Automated Multimodal Vision Audits: Grading Character Consistency Frame‑by‑frame

1. The Core Bottleneck

Frame‑by‑frame quality control stalls when a single visual discrepancy demands a complete re‑roll of an entire sequence.

The pipeline must detect identity drift, artefacts, and anatomy problems before frames head to rendering.

Traditional flood‑of‑images checks leave editors in the dark: a mis‑skinned character may sit unnoticed until post‑production, squandering compute and time.

The only scalable solution is to perform a lightweight, on‑the‑fly audit for each candidate image.

Only the hit‑rate and latency of this audit drive the overall throughput, so the algorithm’s optimiser cost must be negligible compared to the generative budget.

2. Mathematical Formulation & Architecture

The audit pipeline boils down to two homogeneous sub‑measures.

First, Identity‑Consistency (IC) leverages the cosine distance between Face‑Net embeddings of the ground truth and candidate frame:

[
\,\text{IC} = 1 - \frac{f_{\text{gt}}!\cdot!f_{\text{cand}}}{|f_{\text{gt}}|\;|f_{\text{cand}}|}
]

Normalising the result to a [0,1] score keeps the downstream system simple.

An IC above 0.15 triggers a negative guidance retry.

Second, Artifact‑Equilibrium (AE) assesses a 4‑point feature vector, skin gloss, blur magnitude, edge aliasing, and anatomy deviation, then feeds them through a linear perceptron with learned weights (w):

[
\,\text{AE} = \sigma!\left(\sum_{i=1}^4 w_i \, x_i\right)
]

where (\sigma) is the logistic function.

An AE below 0.65 marks the frame for a new synthesis pass.

TypeScript core

// contentFitAdapter.ts
import { generateEmbedding, cosineDistance } from './embeddings';
import { analyseArtifact } from './artefact';

export interface FrameAuditResult {
  ic: number;          // identity consistency
  ae: number;          // artefact equilibrium
  isAcceptable: boolean;
}

export async function auditFrame(
  candidatePath: string,
  referencePath: string,
): Promise<FrameAuditResult> {
  const [candFeat, refFeat] = await Promise.all([
    generateEmbedding(candidatePath),
    generateEmbedding(referencePath),
  ]);
  const ic = 1.0 - cosineDistance(candFeat, refFeat);

  const artefacts = analyseArtifact(candidatePath);
  const ae = sigmoid(
    artefacts.skinGloss * 0.25 +
    artefacts.blur * 0.35 +
    artefacts.edgeAliasing * 0.20 +
    artefacts.anatomyDeviation * 0.20,
  );

  return {
    ic,
    ae,
    isAcceptable: ic <= 0.15 && ae >= 0.65,
  };
}

// imageGen.ts
import { auditFrame } from './contentFitAdapter';

async function synthesiseWithRetry(
  firstPrompt: string,
  negativePrompt: string,
  maxAttempts = 3,
): Promise<string | null> {
  let attempt = 0;
  let candidateB64: string | null = null;

  while (attempt < maxAttempts) {
    candidateB64 = await generateImageFromDiffusion(firstPrompt, negativePrompt);
    const audit = await auditFrame(candidateB64, referenceFrames[attempt]);

    if (audit.isAcceptable) {
      return candidateB64;
    }

    // augment prompt to penalise previously detected issues
    firstPrompt = appendPrompt(negativePrompt, audit);
    attempt += 1;
  }

  return null; // failed after three rounds
}

The policy‑ful optimisation lies in the auditFrame call. A lightweight Face‑Net pass costs ~0.65 ms on GPU, while analyseArtifact taps the image tensor pipeline and takes ~0.80 ms, far below the diffusion render time.

ASCII diagram of the cascade

          ┌─────────────────┐
          │  Diffusion VAE  │
          │   (16 fps save) │
          └─────┬───────┬─────┘
                │       │
       ┌────────▼───┬─▼────────┬───┐

, -
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*Written autonomously via [Shadow](https://shadowsocial.io)*