Build a 3D model from 2–4 ordered front, left, back, and right reference images with configurable output 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-multiview-to-3d',
images: [
'https://example.com/chair-front.png',
'https://example.com/chair-side.png',
'https://example.com/chair-back.png',
],
topology: 'quad',
remesh: true,
symmetry_mode: 'auto',
pbr: true,
texture_prompt: 'Natural walnut wood and woven cream fabric',
texture_image: 'https://example.com/chair-material-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-multiview-to-3d',
'images': [
'https://example.com/chair-front.png',
'https://example.com/chair-side.png',
'https://example.com/chair-back.png',
],
'topology': 'quad',
'remesh': True,
'symmetry_mode': 'auto',
'pbr': True,
'texture_prompt': 'Natural walnut wood and woven cream fabric',
'texture_image': 'https://example.com/chair-material-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-multiview-to-3d",
"images": [
"https://example.com/chair-front.png",
"https://example.com/chair-side.png",
"https://example.com/chair-back.png"
],
"topology": "quad",
"remesh": true,
"symmetry_mode": "auto",
"pbr": true,
"texture_prompt": "Natural walnut wood and woven cream fabric",
"texture_image": "https://example.com/chair-material-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 Multiview to 3D Pricing
| Parameters | Price | Original Price | Discount |
|---|
README
Meshy 7 Multiview-to-3D API (Multi-Image 3D Generation with Optional PBR)
Meshy 7 Multiview-to-3D API generates a 3D model from multiple reference images for game assets, digital design, product visualization, and creative prototyping. Submit up to four views without a required front-left-back-right ordering, then configure topology, target polygon count, remeshing, symmetry, humanoid pose, and optional PBR materials. Available through Flaq AI, the model also supports text or image guidance for the texture workflow.
Key Features of Meshy 7 Multiview-to-3D API
-
Multi-Image Input: Use one or more reference images to provide additional visual context for the generated model.
-
Flexible View Submission: Supply images without a fixed front, left, back, and right ordering requirement.
-
Configurable Topology: Choose triangle or quad topology according to the intended downstream workflow.
-
Target Polygon Control: Set a target polygon count within the supported range to guide mesh density.
-
Remesh, Symmetry & Pose Options: Configure mesh regeneration, symmetry behavior, and supported humanoid poses.
-
Optional PBR & Texture Guidance: Enable PBR material generation and guide texturing with a prompt or reference image.
How to Use Meshy 7 Multiview-to-3D API for Multi-Image 3D Generation on Flaq AI
-
Input: Between one and four required image URLs, plus optional topology, target polygon count, remesh, symmetry, humanoid pose, PBR, texture prompt, and texture image settings.
-
Output: A generated 3D model with optional PBR material textures, returned through the Flaq AI task workflow.
-
View Handling: Submit multiple views of the same subject without a required directional sequence.
-
Mesh Controls: Select triangle or quad topology, configure a target polygon count, and choose whether to remesh the generated geometry.
-
Capabilities: Multi-image-to-3D generation with configurable mesh, pose, symmetry, and texture controls.
Best Use Cases for Meshy 7 Multiview-to-3D API Integration
-
Character & Creature Concepts: Use several consistent views to provide more visual context for a generated base model.
-
Product Visualization: Create 3D concepts from multiple approved product images for review and downstream refinement.
-
Props & Object Modeling: Generate a starting mesh for objects whose shape is shown across several reference angles.
-
Collectibles & Figurine Concepts: Turn multi-angle artwork or photos into base geometry for inspection and fabrication preparation.
-
VFX & Previsualization: Create reference-driven models for layout, scene planning, and early asset development.
Note Use consistent images of the same subject for better cross-view guidance. Generated geometry should still be reviewed before production or fabrication.
Meshy 7 Multiview-to-3D vs Competitors: Comparative Analysis
-
Meshy 7 Multiview-to-3D vs. Tripo Multiview-to-3D
Teams comparing these models should evaluate image requirements, topology controls, target polygon settings, texture workflows, and integration needs. Meshy 7 exposes these options through Flaq AI. -
Meshy 7 Multiview-to-3D vs. Meshy 7 Image-to-3D
Image-to-3D uses one source image, while Multiview-to-3D can accept several views of the same subject. Additional views provide more visual context but do not remove the need to review inferred geometry. -
Meshy 7 Multiview-to-3D vs. Rodin Multiview
The preferred model depends on the subject, available references, and downstream workflow. Meshy 7 combines multi-image input with optional remeshing, pose guidance, symmetry, and PBR controls. -
Meshy 7 Multiview-to-3D vs. SyncDreamer
Developers can compare input workflow, model behavior, geometry controls, texture options, and integration approach. Meshy 7 accepts direct image URLs through the Flaq AI task API. -
Meshy 7 Multiview-to-3D vs. InstantMesh
Product teams should evaluate deployment requirements, mesh controls, result formats, and texture workflow. Meshy 7 is available as a managed API with configurable generation settings.