Turn one reference image into a 3D model with configurable alignment, orientation, texture, material, geometry, scale, and mesh options.
Related Meshy 7 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: 'meshy-v7-image-to-3d',
image_url: 'https://example.com/ceramic-lamp.png',
topology: 'triangle',
remesh: true,
symmetry_mode: 'auto',
pose_mode: 'a-pose',
pbr: true,
texture_prompt: 'Glazed blue ceramic with subtle gold details',
texture_image: 'https://example.com/ceramic-reference.png',
}),
});
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': 'meshy-v7-image-to-3d',
'image_url': 'https://example.com/ceramic-lamp.png',
'topology': 'triangle',
'remesh': True,
'symmetry_mode': 'auto',
'pose_mode': 'a-pose',
'pbr': True,
'texture_prompt': 'Glazed blue ceramic with subtle gold details',
'texture_image': 'https://example.com/ceramic-reference.png',
},
)
response.raise_for_status()
task_id = response.json()['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": "meshy-v7-image-to-3d",
"image_url": "https://example.com/ceramic-lamp.png",
"topology": "triangle",
"remesh": true,
"symmetry_mode": "auto",
"pose_mode": "a-pose",
"pbr": true,
"texture_prompt": "Glazed blue ceramic with subtle gold details",
"texture_image": "https://example.com/ceramic-reference.png"
}'
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"
Meshy 7 Image to 3D Pricing
| Parameters | Price | Original Price | Discount |
|---|
README
Meshy 7 Image-to-3D API (Single-Image 3D Generation with Optional PBR)
Meshy 7 Image-to-3D API generates a 3D model from a single source image for game development, digital design, product visualization, and creative prototyping. Developers can configure topology, target polygon count, remeshing, symmetry, humanoid pose, and optional PBR materials. Available through Flaq AI, the model also accepts text or image guidance for the texture workflow.
Key Features of Meshy 7 Image-to-3D API
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Single-Image 3D Generation: Use one reference image to guide the subject and appearance of a generated 3D model.
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Configurable Topology: Choose triangle or quad topology according to the intended downstream workflow.
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Target Polygon Control: Set a target polygon count within the supported range to guide mesh density.
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Remesh & Symmetry Options: Control whether the mesh is regenerated and how symmetry is applied.
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Humanoid Pose Guidance: Select supported pose options for suitable humanoid subjects.
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Optional PBR & Texture Guidance: Enable PBR material generation and guide texturing with a prompt or reference image.
How to Use Meshy 7 Image-to-3D API for 3D Generation on Flaq AI
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Input: One required image URL, plus optional topology, target polygon count, remesh, symmetry, humanoid pose, PBR, texture prompt, and texture image settings.
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Output: A generated 3D model with optional PBR material textures, returned through the Flaq AI task workflow.
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Mesh Controls: Select triangle or quad topology, configure a target polygon count, and choose whether to remesh the generated geometry.
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Pose Controls: Use the supported humanoid pose settings when the source subject is appropriate for pose guidance.
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Capabilities: Single-image-to-3D generation with configurable mesh, pose, symmetry, and texture controls.
Best Use Cases for Meshy 7 Image-to-3D API Integration
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Concept Art to 3D: Turn a character, prop, or environment reference into a starting model for further development.
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Product Visualization: Create 3D concepts from product images for review and preparation for interactive experiences.
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Character Design: Generate a base model from a suitable character reference, then continue rigging and refinement in downstream tools.
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Collectibles & Figurine Concepts: Convert approved artwork into base geometry for later inspection and fabrication preparation.
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VFX & Previsualization: Create reference-driven models for scene planning, layout, and early asset exploration.
Note A single image does not show every surface of an object, so unseen geometry is inferred and should be reviewed. Ensure source images comply with applicable copyright and intellectual property requirements.
Meshy 7 Image-to-3D vs Competitors: Comparative Analysis
-
Meshy 7 Image-to-3D vs. Tripo Image-to-3D
Teams comparing these models should evaluate input requirements, topology controls, target polygon settings, texture workflows, and integration needs. Meshy 7 exposes these options through Flaq AI. -
Meshy 7 Image-to-3D vs. Rodin Image-to-3D
The preferred model depends on the subject, desired geometry, and downstream workflow. Meshy 7 combines single-image generation with optional remeshing, pose guidance, symmetry, and PBR controls. -
Meshy 7 Image-to-3D vs. Stable Fast 3D
Developers can compare model behavior, available mesh controls, output requirements, and deployment approach. Meshy 7 is available through a managed task API on Flaq AI. -
Meshy 7 Image-to-3D vs. CSM Image-to-3D
Product teams should evaluate subject coverage, geometry controls, texture guidance, and API integration. Meshy 7 accepts one source image and configurable generation settings. -
Meshy 7 Image-to-3D vs. Meshy 7 Multiview-to-3D
Image-to-3D requires one source image. Multiview-to-3D accepts multiple reference images when additional visual angles are available.