Meshy Relief converts photos into embossed or engraved 3D reliefs, making it suitable for decorative models, plaques, medallions, 3D printing, and design workflows, with support for standard 3D and depth-map outputs. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.
Meshy Relief turns a photo into a relief: a flat plate with the subject raised (emboss) or cut in (engrave), for 3D printing and CNC workflows. Download a GLB, a binary STL in millimeters, and the 16-bit depth map.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Photo to convert (JPEG, PNG or WebP, at least 32 x 32 px, up to 20 MB). |
| variant | No | low or high relief style; sets the default carving depth (3 mm or 6 mm). Default: low. |
| should_generate_model | No | Set to false to return only the depth map. Default: true. |
| size_mm | No | Length of the photo's longer side on the model, 20-250 mm. Default: 60. |
| carving_mode | No | emboss or engrave. Default: emboss. |
| carving_depth_mm | No | Carving depth, 0-60 mm. |
| remove_base | No | Cut away the background and build no base plate. Default: false. |
| removal_threshold | No | Depth below which the background is cut, 0.01-1. Used with remove_base. Default: 0.01. |
| base_thickness_mm | No | Base plate thickness, 0.1-10 mm. Leave unset with remove_base. |
| bend_mode | No | off, convex or concave. Default: off. |
| bend_axis | No | xz curls the left and right edges; yz the top and bottom. Default: xz. |
| bend_angle | No | Arc of the bend in degrees, 0-360. Default: 60. |
| mesh_resolution | No | Mesh points along the longer side, 512-2048. Default: 1024. |
| Output | Price per request |
|---|---|
| Relief (model and depth map, or depth map only) | $0.80 |
mesh_resolution makes much larger files: about 40-50 MB GLB and 80-105 MB STL at 1024, and about 150 MB and 300 MB at 2048. A bend can push the STL much higher.remove_base on, a carving depth of 0 gives an empty model that still succeeds and is charged.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/meshy/relief with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Relief below.
set -euo pipefail
: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"
REQUEST_BODY=$(cat <<'JSON'
{
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"variant": "low",
"should_generate_model": true,
"size_mm": 60,
"carving_mode": "emboss",
"remove_base": false,
"removal_threshold": 0.01,
"bend_mode": "off",
"bend_axis": "xz",
"bend_angle": 60,
"mesh_resolution": 1024
}
JSON
)
# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
-X POST "https://api.wavespeed.ai/api/v3/meshy/relief" \
-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
doneconst submitUrl = "https://api.wavespeed.ai/api/v3/meshy/relief";
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 ${apiKey}`,
"Content-Type": "application/json",
},
body: JSON.stringify({
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"variant": "low",
"should_generate_model": true,
"size_mm": 60,
"carving_mode": "emboss",
"remove_base": false,
"removal_threshold": 0.01,
"bend_mode": "off",
"bend_axis": "xz",
"bend_angle": 60,
"mesh_resolution": 1024
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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));
}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 = {
"image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
"variant": "low",
"should_generate_model": True,
"size_mm": 60,
"carving_mode": "emboss",
"remove_base": False,
"removal_threshold": 0.01,
"bend_mode": "off",
"bend_axis": "xz",
"bend_angle": 60,
"mesh_resolution": 1024
}
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/relief", 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)Relief is a Meshy model for 3D asset generation from images, exposed as a REST API on WaveSpeedAI. Meshy Relief converts photos into embossed or engraved 3D reliefs, making it suitable for decorative models, plaques, medallions, 3D printing, and design workflows, with support for standard 3D and depth-map outputs. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.
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-relief.
Relief starts at $0.8 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.
Key inputs: `image`, `base_thickness_mm`, `bend_angle`, `bend_axis`, `bend_mode`, `carving_depth_mm`. 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-relief.
Reported generation time on WaveSpeedAI is around 65 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.
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.