Turn one reference image into a 3D model with configurable alignment, orientation, texture, material, geometry, scale, and mesh options.
Related Tripo3D H3.1 Models
API Examples
Submit Example
const response = await fetch('https://api.flaq.ai/api/v1/3d/task', {
method: 'POST',
headers: {
Authorization: 'Bearer YOUR_API_KEY',
'Content-Type': 'application/json',
},
body: JSON.stringify({
model_name: 'tripo3d-h3.1-image-to-3d',
image_url: 'https://example.com/object.png',
texture_alignment: 'original_image',
orientation: 'default',
texture: true,
pbr: true,
texture_quality: 'standard',
geometry_quality: 'standard',
auto_size: false,
quad: false,
}),
});
const { data } = await response.json();
const taskId = data.task_id;
Polling Example
// Step 2: Poll for results
const taskId = data.task_id;
const pollResult = async (taskId) => {
const res = await fetch(`https://api.flaq.ai/api/v1/3d/${taskId}`, {
headers: { 'Authorization': 'Bearer YOUR_API_KEY' }
});
return res.json();
};
while (true) {
const pollResultData = await pollResult(taskId);
const status = pollResultData.data.task_status;
if (status === 'succeed') {
console.log(pollResultData.data.task_result.files[0].url);
break;
}
if (status === 'failed') {
console.error(pollResultData.data.task_status_msg);
break;
}
await new Promise(resolve => setTimeout(resolve, 10000));
}
Submit Example
import requests
response = requests.post(
'https://api.flaq.ai/api/v1/3d/task',
headers={
'Authorization': 'Bearer YOUR_API_KEY',
'Content-Type': 'application/json',
},
json={
'model_name': 'tripo3d-h3.1-image-to-3d',
'image_url': 'https://example.com/object.png',
'texture_alignment': 'original_image',
'orientation': 'default',
'texture': True,
'pbr': True,
'texture_quality': 'standard',
'geometry_quality': 'standard',
'auto_size': False,
'quad': False,
},
)
response.raise_for_status()
result = response.json()
task_id = result['data']['task_id']
Polling Example
# Step 2: Poll for results
task_id = response.json()['data']['task_id']
poll_url = f"https://api.flaq.ai/api/v1/3d/{task_id}"
while True:
poll_result = requests.get(poll_url, headers={'Authorization': 'Bearer YOUR_API_KEY'}).json()
status = poll_result['data']['task_status']
if status == 'succeed':
print(poll_result['data']['task_result']['files'][0]['url'])
break
if status == 'failed':
print(poll_result['data']['task_status_msg'])
break
time.sleep(10)
Submit Example
curl -X POST "https://api.flaq.ai/api/v1/3d/task" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model_name": "tripo3d-h3.1-image-to-3d",
"image_url": "https://example.com/object.png",
"texture_alignment": "original_image",
"orientation": "default",
"texture": true,
"pbr": true,
"texture_quality": "standard",
"geometry_quality": "standard",
"auto_size": false,
"quad": false
}'
Polling Example
# Step 2: Poll for results
# Replace {task_id} with the task_id returned from the submit response
curl -X GET "https://api.flaq.ai/api/v1/3d/{task_id}" \
-H "Authorization: Bearer YOUR_API_KEY"
Try an example
Tripo H3.1 Image to 3D Pricing
| Parameters | Price | Original Price | Discount |
|---|
README
High-Fidelity Tripo H3.1 Image-to-3D API (AI 3D Reconstruction from Images)
Tripo H3.1 Image-to-3D API by Tripo3D AI converts a single reference image into a detailed 3D asset for developers, creative teams, and 3D production workflows. The Tripo H3.1 model interprets visible structure and surface appearance to generate a mesh with optional textures and PBR materials. Through Flaq AI, teams can integrate image-to-3D reconstruction into product visualization, game-art, e-commerce, and content-generation pipelines.
Key Features of Tripo H3.1 Image-to-3D API
- Single-Image 3D Reconstruction: Transform one clear object image into a 3D model while using the reference as the visual foundation for shape and surface appearance.
- Reference-Aware Geometry: Generate detailed geometry that reflects the subject shown in the source image, making the API useful when a concept, product, or existing visual needs a 3D counterpart.
- Texture and PBR Material Output: Produce optional textured assets with physically based materials for rendering workflows that need more believable surfaces and lighting response.
- Visual Alignment Control: Direct the generation toward preserving the source image’s appearance or prioritizing a coherent 3D structure, depending on the project’s goal.
- Flexible Asset Preparation: Adjust quality, scale, and topology preferences to better suit product rendering, creative iteration, post-production, or asset handoff.
- API-Ready Image-to-3D Workflow: Add image-guided 3D generation to content systems, storefront tools, creative platforms, and automated production flows on Flaq AI.
How to Use Tripo H3.1 Image-to-3D API for 3D Reconstruction on Flaq AI
- Input: A single, clear reference image with the primary subject visibly framed and easy to distinguish from its background.
- Output: A generated 3D mesh with optional textures and PBR materials, returned through the Flaq AI API workflow for rendering, refinement, or delivery.
- Reference Direction: Select whether the output should stay closer to the original image appearance or place more emphasis on geometric coherence.
- Asset Preparation: Flexible quality, sizing, and mesh choices help prepare generated assets for different production requirements.
- Capabilities: Image-guided 3D reconstruction, high-detail mesh generation, texture-aware output, orientation control, and API-based asset delivery through Tripo H3.1.
Best Use Cases for Tripo H3.1 Image-to-3D API Integration
- E-commerce Product Visualization: Turn product imagery into 3D assets for interactive storefronts, promotional scenes, and immersive product presentations.
- Game Art and Virtual Worlds: Convert concept art, prop references, and collectible designs into starting assets for games, virtual experiences, and 3D environments.
- Marketing Creative Production: Reuse a key campaign visual as a 3D object for product films, social content, branded animation, and digital advertising.
- Design and Concept Review: Give stakeholders a spatial version of a design reference, helping teams discuss shape, material direction, and presentation earlier.
- Content Platform Automation: Power image-to-3D features in creative applications and asset-management workflows with a scalable Flaq AI API integration.
Note For the most reliable result, use a clear image of one primary subject with limited occlusion. Make sure you have the rights to upload and transform every source image.
Tripo H3.1 Image-to-3D vs Competitors: Comparative Analysis
-
Tripo H3.1 Image-to-3D vs. Tripo H3.1 Text-to-3D: Image-to-3D starts from a visual reference and is ideal when the target appearance matters. Text-to-3D gives greater freedom when an asset begins as a concept or written description.
-
Tripo H3.1 Image-to-3D vs. Tripo H3.1 Multiview-to-3D: Image-to-3D minimizes input preparation by using one source image. Multiview-to-3D is preferable when several views of the same object are available and more reference coverage is needed.
-
Tripo H3.1 Image-to-3D vs. Meshy Image-to-3D: Meshy offers an accessible image-guided AI 3D workflow. Tripo H3.1 Image-to-3D API focuses on high-fidelity generation with reference alignment, optional PBR materials, and flexible asset-preparation choices for production use.
-
Tripo H3.1 Image-to-3D vs. Rodin Image-to-3D: Rodin is a well-known option for generating 3D assets from visual references. Tripo H3.1 differentiates through its high-detail H3.1 generation path and direct integration into Flaq AI API workflows.
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Tripo H3.1 Image-to-3D vs. Manual 3D Reconstruction: Manual reconstruction can require extensive modeling and texturing time. Tripo H3.1 provides a fast, reference-guided starting point that artists can refine for the final production context.