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Meshy V7 Multi-Image-to-3D reconstructs textured, PBR-ready 3D models from one to four views of the same object, with configurable topology, rigging, and animation options 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

Idle

$1.32per run

ExamplesView all

3D model output

Open preview to inspect the generated asset.

Related Models

README

Meshy V7 Multi-Image to 3D

Meshy V7 Multi-Image to 3D reconstructs a textured 3D mesh from one to four images of the same object taken from different angles. It generates a GLB model and provides additional export formats such as FBX, OBJ, and USDZ when available.

Why Choose This?

  • Multi-image 3D reconstruction
    Generate a 3D model from one to four reference images of the same object.

  • Better shape consistency
    Use multiple views to improve object structure, silhouette, and spatial accuracy compared with single-image generation.

  • Texture generation
    Generate textured 3D models with optional PBR material output.

  • Geometry controls
    Adjust topology, target polygon count, symmetry mode, and remeshing behavior for different production needs.

  • Optional rigging and animation
    Enable humanoid auto-rigging and animation presets when supported by the input object.

  • Multiple export formats
    Receive GLB output with available FBX, OBJ, and USDZ exports.

Parameters

ParameterRequiredDescription
imagesYesOne to four images of the same object from different angles.
topologyNoGeometry topology setting for the generated model.
target_polycountNoTarget polygon count for remeshed geometry.
symmetry_modeNoSymmetry setting used to guide geometry reconstruction.
should_remeshNoWhether to remesh the generated model.
should_textureNoWhether to generate textures for the 3D model.
enable_pbrNoEnable PBR material output when texture generation is used.
pose_modeNoOptional humanoid pose mode.
texture_promptNoText prompt describing the desired texture style or material details.
texture_image_urlNoReference image used to guide texture appearance.
enable_riggingNoEnable humanoid auto-rigging when supported.
rigging_height_metersNoApproximate character height in meters when rigging is enabled.
enable_animationNoEnable an animation preset. Animation requires rigging.
animation_action_idNoAnimation preset ID used when animation is enabled.

How to Use

  1. Upload reference images — Provide one to four images of the same object from different angles.
  2. Configure geometry — Adjust topology, target polygon count, symmetry mode, or remeshing when needed.
  3. Configure textures — Use should_texture, enable_pbr, texture_prompt, or texture_image_url to control material output.
  4. Enable rigging — Use enable_rigging for suitable humanoid characters and optionally specify rigging_height_meters.
  5. Enable animation — Use enable_animation and select an animation_action_id when an animation preset is needed. Rigging must also be enabled.
  6. Submit — Generate the 3D model and download the available exports.

Pricing

ConfigurationFinal Price
Multi-image 3D model$1.32
Multi-image 3D model with auto-rigging$1.54
Multi-image 3D model with auto-rigging and animation$1.672

Example Costs

ConfigurationFinal Price
Standard textured model$1.32
Textured model with auto-rigging$1.54
Textured model with auto-rigging and animation$1.672

Best Use Cases

  • Multi-view object reconstruction — Build 3D models from front, side, rear, or angled object views.
  • Product 3D assets — Convert product photos into textured 3D models for previews and visualization.
  • E-commerce workflows — Create 3D assets for interactive product display.
  • Game and real-time assets — Generate usable 3D models with geometry and remeshing controls.
  • Character workflows — Generate textured character models with optional rigging and animation.
  • Prototype modeling — Turn multiple concept views into a 3D asset for iteration.

Pro Tips

  • Use clear, consistent photos of the same object.
  • Provide front, side, and rear views when possible.
  • Keep lighting, background, and object scale consistent across images.
  • Do not mix different objects or different versions of the same object.
  • Avoid strong reflections, motion blur, occlusion, or heavy shadows.
  • Use texture_prompt to describe material details such as metal, fabric, leather, ceramic, or plastic.
  • Enable rigging only for suitable humanoid characters.
  • Use Meshy V7 Image to 3D when you only have one reference image.
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.

v7 Multi Image To 3d API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/meshy/v7/multi-image-to-3d 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 v7 Multi Image To 3d below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "topology": "triangle",
    "target_polycount": 30000,
    "symmetry_mode": "auto",
    "should_remesh": true,
    "should_texture": true,
    "enable_pbr": false,
    "enable_rigging": false,
    "rigging_height_meters": 1.7,
    "enable_animation": false,
    "animation_action_id": 92
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/meshy/v7/multi-image-to-3d" \
  -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=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 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' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/meshy/v7/multi-image-to-3d";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "images": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ],
        "topology": "triangle",
        "target_polycount": 30000,
        "symmetry_mode": "auto",
        "should_remesh": true,
        "should_texture": true,
        "enable_pbr": false,
        "enable_rigging": false,
        "rigging_height_meters": 1.7,
        "enable_animation": false,
        "animation_action_id": 92
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  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 = {
    "images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "topology": "triangle",
    "target_polycount": 30000,
    "symmetry_mode": "auto",
    "should_remesh": True,
    "should_texture": True,
    "enable_pbr": False,
    "pose_mode": "",
    "enable_rigging": False,
    "rigging_height_meters": 1.7,
    "enable_animation": False,
    "animation_action_id": 92
}

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/v7/multi-image-to-3d", 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 = task.get("urls", {}).get("get") or 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"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

v7 Multi Image To 3d API — Frequently asked questions

What is the v7 Multi Image To 3d API?

v7 Multi Image To 3d is a Meshy model for 3D asset generation from images, exposed as a REST API on WaveSpeedAI. Meshy V7 Multi-Image-to-3D reconstructs textured, PBR-ready 3D models from one to four views of the same object, with configurable topology, rigging, and animation options 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 v7 Multi Image To 3d 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 production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/meshy/meshy-v7-multi-image-to-3d.

How much does v7 Multi Image To 3d cost per run?

v7 Multi Image To 3d starts at $1.32 per run. 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 v7 Multi Image To 3d accept?

Key inputs: `images`, `animation_action_id`, `enable_animation`, `enable_pbr`, `enable_rigging`, `pose_mode`. 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-v7-multi-image-to-3d.

How do I get started with the v7 Multi Image To 3d API?

Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.

Can I use v7 Multi Image To 3d outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Meshy). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Meshy V7 Multi-Image-to-3D API on WaveSpeedAI