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Hunyuan3D V2.1

wavespeed-ai /

Tencent Hunyuan3D v2.1 is a scalable 3D asset-creation system that advances state-of-the-art 3D generation for asset workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-3d
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$0.4cho mỗi lần chạy·~25 / $10

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README

Hunyuan3D V2.1

Hunyuan3D V2.1 is Tencent's advanced image-to-3D generation model. Upload a single photo and the model reconstructs it as a detailed, textured 3D asset — ready for use in games, product visualization, AR/VR, and creative pipelines.

Why Choose This?

  • Single-image 3D reconstruction Generate a fully textured 3D model from just one reference photo — no multi-view capture or manual modeling required.

  • High geometric fidelity Accurately reconstructs object shape, surface detail, and proportions from the input image.

  • Rich texture output Produces clean, high-quality textures that closely match the colors and materials in the source photo.

  • Production-ready assets Output is suitable for direct use in game engines, 3D editors, and AR/VR workflows.

How to Use

  1. Upload your image — provide a clear, well-lit photo of the object you want to convert to 3D.
  2. Submit — the model reconstructs and textures the 3D asset automatically.
  3. Download your generated 3D model.

Pricing

Just $0.40 per generation.

Best Use Cases

  • Game & Interactive Media — Rapidly prototype 3D props and assets from reference photos.
  • E-commerce & Product Visualization — Create 3D product models for interactive viewers and AR try-on experiences.
  • AR/VR Content — Generate real-world object reconstructions for immersive applications.
  • Creative & Design — Turn concept art or physical objects into editable 3D assets without manual modeling.
  • Digital Twins — Quickly digitize physical objects for simulation or archival purposes.

Pro Tips

  • Use a clean, well-lit photo with the object centered and clearly visible for the most accurate reconstruction.
  • Plain or neutral backgrounds help the model focus on the object geometry and texture.
  • Avoid heavily reflective or transparent surfaces — these are harder for single-image reconstruction to handle accurately.
  • Photos taken at a slight angle (rather than perfectly flat-on) tend to produce better depth estimation.

Notes

  • image is the only required field.
  • Ensure image URLs are publicly accessible if using a link rather than a direct upload.
  • Please ensure your content complies with WaveSpeed AI's usage policies.
Lưu ý:Trang web này sử dụng các mô hình AI do bên thứ ba cung cấp.

Hunyuan3d v2.1 API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/hunyuan3d/v2.1 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 Hunyuan3d v2.1 below.

HTTP example
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"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/hunyuan3d/v2.1" \
  -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/wavespeed-ai/hunyuan3d/v2.1";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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 = {
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
}

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/wavespeed-ai/hunyuan3d/v2.1", 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)

Hunyuan3d v2.1 API — Frequently asked questions

What is the Hunyuan3d v2.1 API?

Hunyuan3d v2.1 is a WaveSpeedAI model for 3D asset generation from images, exposed as a REST API on WaveSpeedAI. Tencent Hunyuan3D v2.1 is a scalable 3D asset-creation system that advances state-of-the-art 3D generation for asset 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 Hunyuan3d v2.1 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/wavespeed-ai/hunyuan3d-v2.1.

How much does Hunyuan3d v2.1 cost per run?

Hunyuan3d v2.1 starts at $0.40 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 Hunyuan3d v2.1 accept?

Key inputs: `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/wavespeed-ai/hunyuan3d-v2.1.

How long does Hunyuan3d v2.1 take to generate?

Median end-to-end generation time on WaveSpeedAI is around 126 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Hunyuan3d v2.1 outputs commercially?

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

Hunyuan3D V2.1 | AI Image-to-3D Model API | WaveSpeedAI