Hunyuan3d V2 Multi View
Playground
Try it on WavespeedAI!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
| Parameter | Required | Description |
|---|---|---|
| front_image_url | Yes | Front view image of the subject (upload or public URL). |
| back_image_url | Yes | Back view image of the subject (upload or public URL). |
| left_image_url | Yes | Left view image of the subject (upload or public URL). |
How to Use
- Upload front view image — the front-facing view of your subject.
- Upload back view image — the back-facing view of your subject.
- Upload left view image — the left side view of your subject.
- Click Run and wait for your 3D model to generate.
- 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
| Output | Price |
|---|---|
| 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
| Parameter | Type | Required | Default | Range | Description |
|---|---|---|---|---|---|
| front_image_url | string | Yes | - | - | URL of image to use while generating the 3D model. |
| back_image_url | string | Yes | - | - | URL of image to use while generating the 3D model. |
| left_image_url | string | Yes | - | - | URL of image to use while generating the 3D model. |
Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data.id | string | Unique identifier for the prediction, Task Id |
| data.model | string | Model ID used for the prediction |
| data.outputs | array | Array of URLs to the generated content (empty when status is not completed) |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.has_nsfw_contents | array | Array of boolean values indicating NSFW detection for each output |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |
Result Request Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| id | string | Yes | - | Task ID |
Result Response Parameters
| Parameter | Type | Description |
|---|---|---|
| code | integer | HTTP status code (e.g., 200 for success) |
| message | string | Status message (e.g., “success”) |
| data | object | The prediction data object containing all details |
| data.id | string | Unique identifier for the prediction, the ID of the prediction to get |
| data.model | string | Model ID used for the prediction |
| data.outputs | string | Array of URLs to the generated content (empty when status is not completed). |
| data.urls | object | Object containing related API endpoints |
| data.urls.get | string | URL to retrieve the prediction result |
| data.status | string | Status of the task: created, processing, completed, or failed |
| data.created_at | string | ISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”) |
| data.error | string | Error message (empty if no error occurred) |
| data.timings | object | Object containing timing details |
| data.timings.inference | integer | Inference time in milliseconds |