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Product · Single image · 3 credits

Marketplace-compliant white-background photos, in minutes

Pure white backdrop, correct margins, no shadow drift. Pass Amazon, Walmart, and Shopify image review on first submit.

Standardize a product photo See pricing
White-Background Standardizer sample output

Mixed shots in. Catalog-clean out.

Input Sample input image
Pixfino output Sample output image

Marketplace policy compliance

Each output is checked against current Amazon, Walmart, and Shopify image guidelines before delivery.

Phone photo to PDP-ready

Sellers don't need a studio. Phone snapshot in, marketplace-ready out.

Shadow as a parameter

Some marketplaces ban contact shadows on the main image; the preset handles per-image policy variants.

Who this is for

Marketplace sellers

Job

Pass Amazon/Walmart image review on first try

Friction

Each marketplace's image policy is different and strict.

Brand aggregators

Job

Standardise photo quality across an acquired-brand portfolio

Friction

Each acquired brand ships photos to a different standard.

3PL listing services

Job

Service per-SKU listing prep at scale

Friction

Manual photo cleanup is the throughput ceiling.

Retailer-DAM distributors

Job

Hit retailer image specs without designer help

Friction

Each retailer's DAM has its own intake spec.

How it works

  1. 1

    Upload the product photo

    Phone or studio shot. The renderer isolates the product and replaces the backdrop with pure white.

    Replaces: Photoshop pen-tool isolation per SKU.

  2. 2

    Pick shadow style

    None (Amazon main image), contact (PDP secondary), or drop (lifestyle accent).

    Replaces: Photographer-set shadow placement.

  3. 3

    Generate

    One render, ~20 seconds. Output is RGB 255/255/255 backdrop with correct product margins.

    Replaces: Manual cleanup pass per SKU.

  4. 4

    Bulk-upload to marketplace

    Pass image-quality review on first submit; failed uploads drop to near-zero.

    Replaces: Bounce-and-resubmit cycles.

Pixfino vs traditional production

Traditional studio Stock + retouch Pixfino
Lead time 1-3 days Same-day Under 1 minute
Per-photo cost $15-$50 $8-$15 3 credits
Iterations Re-shoot or re-edit Slow Unlimited
Compliance Designer-checked Generic Built to current spec
Scale ceiling 100-200/day Slow Thousands per hour
Revisions Charged per pass Re-purchase Free re-runs

Questions and answers

Will it pass Amazon image review?
Output is built against Amazon's published main-image guidelines. Always confirm against the latest policy version.
Will the product silhouette stay clean?
Yes — the renderer detects edges precisely and avoids the halo artefacts manual masking sometimes produces.
Can I batch this across my catalog?
Yes. POST a list of product photos via the API; one credit cost per image.
What about shadows for hero PDP shots?
Shadow style is a parameter — none, contact, or drop — so you can match per-marketplace policy.
Does this handle reflective products?
Yes — chrome, glass, and polished surfaces are isolated cleanly.
What resolution do I get?
1024px on the longest edge; 2x via the API for retailer DAM intake.

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Measured quality

Full QC report →

White-Background Standardizer

oaktree/image-edit · $0.04/image

high pass-rate baseline

The easiest of the shoe presets and our highest pass rate — included as an honest contrast so the harder presets read in context, not as a wall of identical 99%s.

Background compliance

target ≥ 95% PDP pass

93.3% below target
n=99 · 95% CI 91.77–94.88 · BiRefNet_lite (transformers 4.52.4) + numpy 2.4.6 · pipeline v3.0 · set shoes-v3 · measured 2026-06-13

Color fidelity (ΔE 2000)

target ≤ 2.0 ΔE (imperceptible)

3.79 ΔE below target
n=99 · 95% CI 3.00–4.59 · BiRefNet_lite (transformers 4.52.4) + colour-science 0.4.7 · pipeline v3.0 · set shoes-v3 · measured 2026-06-13

Frame occupancy

target ≥ 85% of frame (Amazon spec)

84.4% below target
n=99 · 95% CI 83.02–85.90 · BiRefNet_lite (transformers 4.52.4) + numpy 2.4.6 · pipeline v3.0 · set shoes-v3 · measured 2026-06-13

Silhouette preservation

target ≥ 0.90 shape IoU

0.910 IoU pass
n=99 · 95% CI 0.88–0.95 · BiRefNet_lite (transformers 4.52.4) + numpy 2.4.6 · pipeline v3.0 · set shoes-v3 · measured 2026-06-13

Perceptual drift (LPIPS)

target threshold pending calibration

0.198
n=99 · 95% CI 0.16–0.23 · piq.LPIPS (mask-aligned crop) 0.8.0 · pipeline v3.0 · set shoes-v3 · measured 2026-06-13

Auto-QC first-pass rate

target ≥ 85% first pass

46.4% below target
n=99 · 95% CI 36.95–56.24 · pixfino-qc-gate n/a · pipeline v3.0 · set shoes-v3 · measured 2026-06-13

Phone snap in, marketplace-ready PDP out. Pass review on first submit.

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