V2Fun API Docs

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Remesh ​

A critical step in the 3D production pipeline. Models directly from AI often contain messy triangles that are inefficient and hard to rig. This service converts messy meshes into clean quad-based topology for better performance and animation.

Typical Application Scenarios ​

  • LOD Optimization: Optimize ultra-high-poly models into low-poly versions suitable for real-time mobile rendering.
  • Animation Pipeline: Generate clean quad meshes to ensure smooth, natural deformation after skeletal binding.

API Reference used on this page POST /3d_models/remeshings

3D Model Input Formats

Model parameters accept three formats:

  • Previous task result: Use the asset_path from a text/image-to-3D task directly — no re-upload needed
  • Base64 encoding: Pass the raw base64-encoded .glb file content (no data: prefix)
  • Public URL: Any accessible .glb or supported format file link

Basic Examples ​

bash
curl -X POST 'https://api.v2fun.ai/api/v1/3d_models/remeshings' \
  -H 'Authorization: Bearer $V2FUN_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
      "input_model": "models/example.glb"
    }'
javascript
const response = await fetch('https://api.v2fun.ai/api/v1/3d_models/remeshings', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer $V2FUN_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    "input_model": "models/example.glb"
  }),
});
const data = await response.json();
python
import requests

resp = requests.post(
    'https://api.v2fun.ai/api/v1/3d_models/remeshings',
    headers={
        'Authorization': 'Bearer $V2FUN_API_KEY',
        'Content-Type': 'application/json',
    },
    json={
        "input_model": "models/example.glb"
    },
)
print(resp.json())
java
import java.net.URI;
import java.net.http.*;

HttpClient client = HttpClient.newHttpClient();
String body = "{\"input_model\":\"models/example.glb\"}";
HttpRequest request = HttpRequest.newBuilder()
    .uri(URI.create("https://api.v2fun.ai/api/v1/3d_models/remeshings"))
    .header("Authorization", "Bearer $V2FUN_API_KEY")
    .header("Content-Type", "application/json")
    .POST(HttpRequest.BodyPublishers.ofString(body))
    .build();
HttpResponse<String> response = client.send(
    request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());

Core Use Cases ​

LOD Mesh Reduction ​

By setting mesh_count, you can generate different precision versions of the same asset. For example, reduce a 500k-poly model to 10k polys in one click.

bash
curl -X POST 'https://api.v2fun.ai/api/v1/3d_models/remeshings' \
  -H 'Authorization: Bearer $V2FUN_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
      "input_model": "https://asset.v2fun.ai/upload/remesh-input-robot.glb",
      "mesh_count": 10000
    }'
javascript
const response = await fetch('https://api.v2fun.ai/api/v1/3d_models/remeshings', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer $V2FUN_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    "input_model": "https://asset.v2fun.ai/upload/remesh-input-robot.glb",
    "mesh_count": 10000
  }),
});
const data = await response.json();
python
import requests

resp = requests.post(
    'https://api.v2fun.ai/api/v1/3d_models/remeshings',
    headers={
        'Authorization': 'Bearer $V2FUN_API_KEY',
        'Content-Type': 'application/json',
    },
    json={
        "input_model": "https://asset.v2fun.ai/upload/remesh-input-robot.glb",
        "mesh_count": 10000
    },
)
print(resp.json())
java
import java.net.URI;
import java.net.http.*;

HttpClient client = HttpClient.newHttpClient();
String body = "{\"input_model\":\"https://asset.v2fun.ai/upload/remesh-input-robot.glb\",\"mesh_count\":10000}";
HttpRequest request = HttpRequest.newBuilder()
    .uri(URI.create("https://api.v2fun.ai/api/v1/3d_models/remeshings"))
    .header("Authorization", "Bearer $V2FUN_API_KEY")
    .header("Content-Type", "application/json")
    .POST(HttpRequest.BodyPublishers.ofString(body))
    .build();
HttpResponse<String> response = client.send(
    request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());
Input Model
Output

Quad Topology Conversion ​

Switch topology to quad. Quads are preferred by animators because they follow the object's deformation forces perfectly, eliminating the "stretching" feel in animations.

mesh_topology: triangle · quad

bash
curl -X POST 'https://api.v2fun.ai/api/v1/3d_models/remeshings' \
  -H 'Authorization: Bearer $V2FUN_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
      "input_model": "https://asset.v2fun.ai/upload/remesh-input-robot.glb",
      "mesh_topology": "quad",
      "mesh_count": 5000
    }'
javascript
const response = await fetch('https://api.v2fun.ai/api/v1/3d_models/remeshings', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer $V2FUN_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    "input_model": "https://asset.v2fun.ai/upload/remesh-input-robot.glb",
    "mesh_topology": "quad",
    "mesh_count": 5000
  }),
});
const data = await response.json();
python
import requests

resp = requests.post(
    'https://api.v2fun.ai/api/v1/3d_models/remeshings',
    headers={
        'Authorization': 'Bearer $V2FUN_API_KEY',
        'Content-Type': 'application/json',
    },
    json={
        "input_model": "https://asset.v2fun.ai/upload/remesh-input-robot.glb",
        "mesh_topology": "quad",
        "mesh_count": 5000
    },
)
print(resp.json())
java
import java.net.URI;
import java.net.http.*;

HttpClient client = HttpClient.newHttpClient();
String body = "{\"input_model\":\"https://asset.v2fun.ai/upload/remesh-input-robot.glb\",\"mesh_topology\":\"quad\",\"mesh_count\":5000}";
HttpRequest request = HttpRequest.newBuilder()
    .uri(URI.create("https://api.v2fun.ai/api/v1/3d_models/remeshings"))
    .header("Authorization", "Bearer $V2FUN_API_KEY")
    .header("Content-Type", "application/json")
    .POST(HttpRequest.BodyPublishers.ofString(body))
    .build();
HttpResponse<String> response = client.send(
    request, HttpResponse.BodyHandlers.ofString());
System.out.println(response.body());
Input Model
Output