Seedance 2.5 Now Live | Try in Video Generator →
Home/Explore/Hitem3d/Hi3d V3.0/Multi View To 3d

hitem3d/

Hi3D V3.0 Multi-View-to-3D reconstructs a detailed 3D mesh from canonical front, back, left, and right reference views, with optional textures, PBR materials, and multiple export formats 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

$2.3per run

ExamplesView all

3D model output

Open preview to inspect the generated asset.

Related Models

README

Hi3D V3.0 Multi-view to 3D

Hi3D V3.0 Multi-view to 3D reconstructs one 3D asset from canonical views of the same object. Provide a front image and optionally add back, left, and right views to improve geometry accuracy, shape consistency, and texture quality.

Why Choose This?

  • Multi-view 3D reconstruction
    Build a 3D model from multiple views of the same object for stronger shape consistency.

  • Canonical-view workflow
    Start with a required front view, then add back, left, and right images when available.

  • Higher geometry reliability
    Multi-view input helps the model better preserve structure, silhouette, and spatial detail.

  • Texture and PBR support
    Generate textured output and optional PBR material maps for more production-ready assets.

  • Configurable geometry
    Control target face count for different quality and downstream workflow needs.

  • Multiple export formats
    Export generated models as glb, obj, stl, fbx, or usdz.

  • Shading control
    Adjust de-shading strength to improve texture and material quality.

Parameters

ParameterRequiredDescription
front_imageYesFront-view PNG, JPEG, or WebP image of the object. Maximum file size: 20 MB.
back_imageNoOptional back-view image of the same object.
left_imageNoOptional left-view image of the same object.
right_imageNoOptional right-view image of the same object.
resolutionNoGeneration quality mode: 2048quality for faster generation or 2048master for the highest quality.
enable_textureNoGenerate textures in addition to geometry. Default: true.
enable_pbrNoGenerate PBR material maps with the texture. Default: true. Ignored when enable_texture is disabled.
face_countNoTarget face count for the generated mesh. Range: 100000–5000000.
export_formatNoOutput format: glb, obj, stl, fbx, or usdz.
shadingNoDe-shading strength. Range: 0–1. Default: 0.5.

How to Use

  1. Upload the front image — Provide a clear front-view image of the object.
  2. Add more views optional — Upload back, left, and right images of the same object when available.
  3. Choose resolution — Use 2048quality for faster generation or 2048master for higher-quality output.
  4. Configure texture optional — Keep enable_texture enabled when textured output is needed.
  5. Configure PBR optional — Keep enable_pbr enabled when material maps are required.
  6. Set face count optional — Choose a target face count based on your geometry detail needs.
  7. Choose export format — Select glb, obj, stl, fbx, or usdz.
  8. Adjust shading optional — Use shading to control de-shading strength.
  9. Submit — Generate the 3D model and retrieve the output through the standard WaveSpeed prediction response.

Pricing

Pricing is based on selected resolution, texture generation, and PBR generation.

ResolutionGeometry onlyTextureTexture + PBR
2048quality$1.98$2.20$2.31
2048master$9.68$9.90$10.01

PBR is only generated and priced when texture generation is enabled.

Best Use Cases

  • Multi-view object reconstruction — Build a more accurate 3D model from multiple angles of the same object.
  • Product 3D assets — Convert product photos into textured 3D models for previews and visualization.
  • E-commerce workflows — Create 3D assets for interactive product display.
  • Design and prototyping — Turn reference photos into editable 3D assets for iteration.
  • Game and real-time pipelines — Generate geometry with configurable face counts for downstream optimization.
  • Production-ready exports — Use GLB, OBJ, STL, FBX, or USDZ depending on your workflow.

Pro Tips

  • Use images of the same object with consistent lighting, framing, and scale.
  • Canonical input order is front, back, left, then right.
  • Provide as many clean views as possible for better shape reconstruction.
  • Avoid cluttered backgrounds, reflections, heavy shadows, occlusion, or motion blur.
  • Use 2048quality for faster iteration and 2048master for higher-quality final output.
  • Enable textures when color and surface appearance matter.
  • Enable PBR when the output will be used in rendering, visualization, or game-engine workflows.
  • Set face_count lower for lightweight assets and higher for more detailed geometry.

Notes

  • front_image is required.
  • Use canonical views of the same object for the best results.
  • PBR is only generated and billed when texture generation is enabled.
  • The safety checker follows the upstream default and is not exposed in the public form.
  • The generated model file is returned through the standard WaveSpeed prediction response.

Related Models

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.

Hi3d v3.0 Multi View To 3d API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/hitem3d/hi3d-v3.0/multi-view-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 Hi3d v3.0 Multi View To 3d below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "front_image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "resolution": "2048quality",
    "enable_texture": true,
    "enable_pbr": true,
    "face_count": 2000000,
    "export_format": "glb",
    "shading": 0.5
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/hitem3d/hi3d-v3.0/multi-view-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/hitem3d/hi3d-v3.0/multi-view-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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "front_image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "resolution": "2048quality",
        "enable_texture": true,
        "enable_pbr": true,
        "face_count": 2000000,
        "export_format": "glb",
        "shading": 0.5
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "front_image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "resolution": "2048quality",
    "enable_texture": True,
    "enable_pbr": True,
    "face_count": 2000000,
    "export_format": "glb",
    "shading": 0.5
}

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/hitem3d/hi3d-v3.0/multi-view-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)

Hi3d v3.0 Multi View To 3d API — Frequently asked questions

What is the Hi3d v3.0 Multi View To 3d API?

Hi3d v3.0 Multi View To 3d is a Hitem3d model for 3D asset generation from images, exposed as a REST API on WaveSpeedAI. Hi3D V3.0 Multi-View-to-3D reconstructs a detailed 3D mesh from canonical front, back, left, and right reference views, with optional textures, PBR materials, and multiple export formats 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 Hi3d v3.0 Multi View 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/hitem3d/hitem3d-hi3d-v3.0-multi-view-to-3d.

How much does Hi3d v3.0 Multi View To 3d cost per run?

Hi3d v3.0 Multi View To 3d starts at $2.30 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 Hi3d v3.0 Multi View To 3d accept?

Key inputs: `resolution`, `back_image`, `enable_pbr`, `enable_texture`, `export_format`, `face_count`. 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/hitem3d/hitem3d-hi3d-v3.0-multi-view-to-3d.

How do I get started with the Hi3d v3.0 Multi View 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 Hi3d v3.0 Multi View To 3d outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Hitem3d). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

Hi3D V3.0 Multi-View-to-3D API on WaveSpeedAI