Use case · Coats and outerwear

Generate on-model photos of coats and outerwear with the layers visible

Supply front, back and lining photos of each coat. Your assistant loads Product page set, shows the credit estimate, and the AI reviewer compares layers and closures with your references.

Set up Uwear MCPCheck model credit rates
Model wearing a suede zip jacket, full-body front on the Pure White studio, generated with nano-banana-pro through the Product page set template

Front hero from the Product page set template: suede zip jacket on the Pure White studio, generated with nano-banana-pro through the Uwear MCP.

Production signals

  • Source photos guide the brief
  • Cost approval before generation
  • Automatic QA, on or off

Mechanism

How does the assistant build the photo set?

Model wearing the same suede zip jacket, full-body back camera edit on the Pure White studio, generated with nano-banana-pro through the Product page set template
Back view from the same Product page set run: a camera edit of the front hero, so the model, light, and studio carry over. Generated with nano-banana-pro through the Uwear MCP.

Ask the Uwear MCP to load Product page set. The front photo becomes the source for camera edits; supplied lining, closure and layer references guide the brief.

Workflow

The production loop

  1. Prepare the source views

    Supply front and back flats, open-front and lining photos, hood states, cuffs, fastenings and the intended underlayer. Add a photographed shape reference for structured coats.[1][2][3][5]

  2. Approve the proposed template

    Start with Product page set and Basic white photoshoot. Ask the assistant to flag missing views and show the credit estimate before generation.[10][11]

  3. Compare the results

    QA is automatic and toggleable: when on, an AI reviewer compares results with source photos and retries flagged results.[10]

Outcomes

What this unlocks

  • Check the open and closed coat

    Treat lining, hood states and underlayers as separate source-backed review requests, not default template views.[1][2][3][5]

  • Check the destination brief

    Zalando asks for special features such as removable hoods and inner pockets to be shown. Its AI permission and real-person model wording remain unresolved. Confirm image-slot eligibility with the destination before submitting a generated white-studio set.[6][7]

Run it from your assistant

Run this photoshoot from your AI assistant with a Uwear template.

Connect the Uwear MCP server to ChatGPT, Claude, or Codex and ask for the Product page set template. It is a batch workflow that runs once per garment: front, upper-body, and back product-page shots on a white studio background, plus a 360-degree video, from one garment photo. Any Uwear account can load it by name, and the assistant shows a costed brief in credits before generation starts.

For a separate campaign, propose Instagram drop with Nostalgic Coastal Collage: three lifestyle photos and a clip per item. Check the brand’s seasonal brief before changing the setting.

Copy this prompt into your assistant

Use the Uwear MCP to prepare a Product page set for my uploaded coats with Basic white photoshoot on Pure White, using nano-banana-pro. Use the supplied front, back, lining and closure references. Identify missing source views. Show the costed brief in credits and wait for my approval before generation. Turn the AI reviewer on to compare results with source photos and retry flagged results. Return image and video URLs. Treat lining and hood checks as review requests, not guaranteed template views.

Set up the MCP server

What one run does, per garment

  1. Generate the front hero

    One full-body front photo of the garment on the template model, in the Basic white photoshoot art direction on the Pure White studio location.

  2. Edit to the upper-body crop

    A camera edit that reframes the front result to an upper-body front frame instead of generating a new image, so the crop comes from the same session as the hero.

  3. Edit to the back view

    A second camera edit of the front result to a full-body back frame, so the product page gets the back of the garment from the same session.

  4. Render the 360 turn

    A 5-second Kling 3 Pro video from the front photo: the model turns a slow, full 360 degrees in place, garment readable throughout.

Outputs per garment

  • Full-body front photo, 2K, 2:3
  • Upper-body front photo, 2K, 2:3
  • Full-body back photo, 2K, 2:3
  • 5-second 360-degree video

The setup the template pins

Template kind
Batch workflow: runs once per garment row
System key
demo_full_pdp_test_white_studio
Art direction
Basic white photoshoot is the starting system art direction. For the separate Instagram drop proposal, keep Nostalgic Coastal Collage.
Location
Pure White (system studio location)
Model
The Uwear system model by default; the assistant can swap in one of your saved models when it proposes the brief
Image model
nano-banana-pro, 2K, 2:3
Video model
Kling 3 Pro, 5 seconds
QA
Optional. With the AI reviewer on, each result is checked against the source product photo and flagged results are retried
Approval
The assistant shows a costed brief in credits before anything generates

Frequently asked questions

Supply front and back flats, open-front and lining photos, hood states, cuffs, fastenings and the intended underlayer. Add a photographed shape reference for structured coats.[1][2][3][5]

Product page set with Basic white photoshoot on the Pure White studio. The default batch workflow makes front, upper-body and back photos at 2K, 2:3 with nano-banana-pro, plus a 5-second 360 video. The assistant shows a costed brief before approval.[10][11]

Compare the source views separately. Burberry describes a storm shield and lining, Max Mara describes lapel stitching, and Patagonia lists hood and zip features. NotShot reports shoulder and lapel problems in its jacket workflow; no independent failure rate was measured.[1][2][3][4]

Zalando asks for special features such as removable hoods and inner pockets to be shown. Its AI permission and real-person model wording remain unresolved. Confirm image-slot eligibility with the destination before submitting a generated white-studio set.[6][7]

QA is automatic and toggleable: when on, an AI reviewer compares results with source photos and retries flagged results. Use the Uwear MCP first. Credits cost $0.10 per credit; see /models for model rates. The REST API is the fallback for pure automation, with polling or callbacks. Studio is the manual path.[10]

NotShot is a fit for evaluating jacket-specific source framing and shoulder checks. Picjam is a fit for structured-jacket preparation guidance. Their output quality was not tested in this research.[4][5]

Sources and access dates

  1. [1]
  2. [2]
  3. [3]
  4. [4]
    NotShot: jacket workflow

    Accessed 2026-09-07

  5. [5]
  6. [6]
    Zalando: apparel image guide

    Accessed 2026-09-07

  7. [7]
    Zalando: AI and model rules

    Accessed 2026-09-07

  8. [10]
  9. [11]

Next step

Map this workflow into your catalog operation.

Start with the use case, then decide whether the right access path is Studio, API, MCP, or a custom enterprise rollout.

Set up Uwear MCPCheck model credit rates