Use case · Bags and leather goods

Generate on-model photos of bags with the straps and hardware legible

Supply front, back, side, base and interior photos plus a carried reference. Your assistant proposes a Product page set test with a back camera edit, the shape we ran on staging on one backpack.

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Model carrying a red technical backpack, 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: red technical backpack 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 carrying the same red technical backpack, 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.

Observed on staging, 2026-09-08: a Product page set shape completed a full-body front generation and full-body back camera edit for one backpack worn on the shoulders, with the back asset attached (brief 431).

Workflow

The production loop

  1. Prepare the source views

    Supply front, back, side, base and interior photos, strap attachments, closure and hardware details, dimensions and an approved carried reference. Placement depends on those assets.[1][2][3]

  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.[9][8]

  3. Compare the results

    QA is automatic and toggleable: when on, an AI reviewer compares results with source photos and retries flagged results.[9]

Outcomes

What this unlocks

  • Check the carrying mode and hidden views

    Coach describes hand, shoulder and crossbody options; Longchamp and Polène describe interior details. Supply each relevant carrying reference and interior photo instead of inferring them from a closed front.[1][2][3]

  • Check the destination brief

    Zalando’s bags guide requires a primary packshot and two more compliant images. It asks for filled bags and straps that do not cross the whole bag. Interior and detail views are examples, not a universal angle list. Confirm carried-image slots and unresolved AI/model eligibility.[5][6]

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 Noir Chrome Shadow Studio: three lifestyle photos and a clip per item. This campaign pairing still needs an asset and placement test.

Copy this prompt into your assistant

Use the Uwear MCP to assess a test for my uploaded bag. Review front, back, side, base, interior and strap references plus dimensions and a carried reference. Propose a Product page set test with Basic white photoshoot on Pure White, using nano-banana-pro. For a backpack, propose full-body front and back camera-edit views with the back asset attached. Flag unverified interior or in-hand views; do not infer them from a closed front. Show the costed test in credits and wait for approval. Turn the AI reviewer on to compare results with source photos and retry flagged results. Return result URLs and identify which requested views were tested.

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, swap to Noir Chrome Shadow Studio. The specification below describes the default garment template, not the outputs verified by this test.
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, back, side, base and interior photos, strap attachments, closure and hardware details, dimensions and an approved carried reference. Placement depends on those assets.[1][2][3]

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. This is the default garment specification; the test below covers only the stated views.[9][8]

One technical backpack worn on the shoulders, on staging on 2026-09-08 (brief 431). The full-body front and full-body back camera edit completed with the back asset attached. Handbags in hand and interiors were not tested. This does not establish a native bag template.

Zalando’s bags guide requires a primary packshot and two more compliant images. It asks for filled bags and straps that do not cross the whole bag. Interior and detail views are examples, not a universal angle list. Confirm carried-image slots and unresolved AI/model eligibility.[5][6]

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.[9]

PicCopilot is a fit for evaluating a named handbag-to-model workflow. Its fetched page did not document minimum source angles or interior and strap requirements. In-hand contact was not tested. A separate public LoRA bag report describes shape and strap drift; it is one workflow, not a benchmark.[10][4]

Sources and access dates

  1. [1]
  2. [2]
  3. [3]
  4. [4]
  5. [5]
    Zalando: bag image guide

    Accessed 2026-09-07

  6. [6]
    Zalando: AI and model rules

    Accessed 2026-09-07

  7. [8]
  8. [9]
  9. [10]
    PicCopilot: handbag tool

    Accessed 2026-09-07

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.

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