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Meshy N1 Retexture API

meshy/n1/retexture

Meshy N1 Retexture applies new textures to existing 3D models using reference images, enabling reference-guided material and style transfer for game assets, product visualization, 3D design, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Image to 3D
Input

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preview

Idle

3D

$0.4per run·~25 / $10

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3D

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README

Meshy N1 Retexture

Meshy N1 Retexture generates a new texture for an existing 3D model from one to four reference images. Explore materials, colors and surface details that match a picture, and keep the model's UV layout if you want.

Why Choose This?

  • Image-guided texturing — Match the look of one to four reference images.
  • Keep your UVs — Reuse the model's existing UV layout or generate a new one.
  • Simple pricing — One price per request.

Parameters

ParameterRequiredDescription
modelYesThe 3D model to texture (GLB, GLTF, FBX, OBJ or STL).
texture_imageNoOne reference image for the texture style. Use this or images.
imagesNoOne to four reference images of the object. Use this or texture_image.
enable_original_uvNoReuse the model's existing UV layout. Default: true.

How to Use

  1. Upload the 3D model you want to texture.
  2. Provide one reference image (texture_image) or one to four (images).
  3. Choose whether to reuse the model's UV layout.
  4. Submit the request and download the textured model.

Pricing

OutputPrice per model
2k base color texture$0.40

The reference images and the UV setting do not add separate charges.

Notes

  • N1 textures from images only: it does not take a text prompt, PBR maps or other texture sizes. The base color texture is 2k.
  • Provide either texture_image or images, not both.
  • For multiple reference images, use different views of the same object. The first image is the main front view; the remaining views can be in any order.
  • A reference image with a substantially different object shape may be harder to match to the model.
Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

N1 Retexture API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/meshy/n1/retexture with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for N1 Retexture below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "model": "example",
    "enable_original_uv": true
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/meshy/n1/retexture" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    *) sleep 2 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/meshy/n1/retexture";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "model": "example",
        "enable_original_uv": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "model": "example",
    "enable_original_uv": True
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/meshy/n1/retexture", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout", "deleted"}:
        raise RuntimeError(result)
    time.sleep(2)

N1 Retexture API — Frequently asked questions

What is the N1 Retexture API?

N1 Retexture is a Meshy model for 3D asset generation from images, exposed as a REST API on WaveSpeedAI. Meshy N1 Retexture applies new textures to existing 3D models using reference images, enabling reference-guided material and style transfer for game assets, product visualization, 3D design, and production workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the N1 Retexture API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates Python, JavaScript, and cURL examples for submitting requests and polling results. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/meshy/meshy-n1-retexture.

How much does N1 Retexture cost per run or per second?

N1 Retexture starts at $0.4 per run or per second. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does N1 Retexture accept?

Key inputs: `images`, `enable_original_uv`, `model`, `texture_image`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/meshy/meshy-n1-retexture.

How long does N1 Retexture take to generate?

Reported generation time on WaveSpeedAI is around 40 seconds per request. This is an estimate, not a latency guarantee; queue time and input settings can change the total wait. live status is visible in the prediction record.

Can I use N1 Retexture outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Meshy). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.

llms.txt — meshy/n1/retexture for AI agents and LLMs