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Hunyuan3d V2 Multi View

Hunyuan3d V2 Multi View

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Hunyuan3D V2 Multi-View generates accurate 3D reconstructions from multiple images. Tencent-developed and available on WaveSpeedAI. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Hunyuan3D V2 Multi-View — Images-to-3D

Hunyuan3D V2 Multi-View is part of Tencent’s open-source Hunyuan3D-2 series — a state-of-the-art 3D generation system that creates high-fidelity 3D models from multiple reference images. Provide front, back, and left views of your subject for accurate 3D reconstruction with detailed geometry.


Why It Stands Out

  • Multi-view input: Uses front, back, and left images for more accurate 3D reconstruction.
  • High-precision geometry: Multiple viewpoints enable better shape capture and detail.
  • High-fidelity output: Produces detailed 3D models with accurate geometry and high-resolution (4K) textures.
  • Fast generation: Completes model generation in as fast as 30 seconds.
  • Decoupled architecture: Separates geometry generation and texture synthesis for improved quality.

Technical Highlights

The Hunyuan3D-2 system adopts a separated process of geometry generation + texture synthesis:

  • Geometry Generation (Hunyuan3D-DiT): Based on a flow diffusion model that generates untextured 3D geometric models, with 2.6B parameters, capable of precisely extracting geometric information from input images.

  • Texture Synthesis (Hunyuan3D-Paint): Adds high-resolution (4K) textures to geometric models, with 1.3B parameters, supporting multi-view diffusion generation technology to ensure realistic textures and consistent lighting.

By decoupling shape and texture generation, the system effectively reduces complexity and improves generation quality.


Performance and Efficiency

  • Fast Generation: Completes model generation in as fast as 30 seconds.
  • Accelerated Inference: The optimized version shortens inference time by 50% through guidance distillation techniques.
  • Multi-modal Support: Compatible with various integrations including Blender plugins and Gradio applications.

Parameters

ParameterRequiredDescription
front_image_urlYesFront view image of the subject (upload or public URL).
back_image_urlYesBack view image of the subject (upload or public URL).
left_image_urlYesLeft view image of the subject (upload or public URL).

How to Use

  1. Upload front view image — the front-facing view of your subject.
  2. Upload back view image — the back-facing view of your subject.
  3. Upload left view image — the left side view of your subject.
  4. Click Run and wait for your 3D model to generate.
  5. Preview and download the result.

Best Use Cases

  • Character Modeling — Create 3D characters from reference artwork or photos.
  • Game Assets — Generate game-ready 3D models from concept art.
  • Product Visualization — Generate 3D models of products from multiple angles.
  • 3D Printing — Generate printable models from multi-view references.
  • AR/VR Content — Create 3D objects for immersive experiences.
  • Animation — Build 3D characters and props from design sheets.

Pricing

OutputPrice
Per 3D model$0.02

Pro Tips for Best Quality

  • Use consistent lighting across all three reference images.
  • Ensure the subject is centered and at similar scale in each view.
  • Use clean backgrounds (ideally transparent or solid color) for better results.
  • Align viewpoints accurately — front, back, and left should be 90° apart.
  • For characters, include clear details of face, clothing, and accessories in each view.

Notes

  • Ensure all uploaded image URLs are publicly accessible.
  • Processing time varies based on current queue load.
  • Please ensure your content complies with usage guidelines.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result


# Submit the task
curl --location --request POST "https://api.wavespeed.ai//api/v3/wavespeed-ai/hunyuan3d/v2-multi-view" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}" \
--data-raw '{}'

# Get the result
curl --location --request GET "https://api.wavespeed.ai//api/v3/predictions/${requestId}/result" \
--header "Authorization: Bearer ${WAVESPEED_API_KEY}"

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
front_image_urlstringYes--URL of image to use while generating the 3D model.
back_image_urlstringYes--URL of image to use while generating the 3D model.
left_image_urlstringYes--URL of image to use while generating the 3D model.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayArray of URLs to the generated content (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.has_nsfw_contentsarrayArray of boolean values indicating NSFW detection for each output
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction, the ID of the prediction to get
data.modelstringModel ID used for the prediction
data.outputsstringArray of URLs to the generated content (empty when status is not completed).
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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