tripo-h3.1/image-to-3d

image-to-3D

Tripo H3.1 Image-to 3D API by TRIPO

tripo-h3.1/image-to-3d

Image-to-3d

Generate high-quality 3D models from a single image using Tripo H3.1.

Tripo H3.1 Image-to 3D is developed by VAST AI. Atlas Cloud (operated by Atlas Cloud AI LLC) provides access to it and does not own it. All trademarks belong to their respective owners.

INPUT

Image url *

You can drag and drop a file or click to upload

MAX:1

Texture

Pbr

Texture quality

Geometry quality

Texture alignment

Orientation

Additional Settings

OUTPUT

Idle

Your generated 3D model will appear here

Configure your settings and click Run to get started

Your request will cost $0.1 per run. For $10 you can run this model approximately 100 times.

Parameters

Code Example
import requests
import time

# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "tripo-h3.1/image-to-3d",  # Required. model name
    "image_url": "example_value",  # Required. URL of the input image used to create the 3D model
    "face_limit": 1000,  # Target mesh face count. (min: 1000, max: 2000000)
    "texture": True,  # Enable texture generation
    "pbr": True,  # Generate PBR (Physically Based Rendering) materials
    "texture_quality": "standard",  # Texture resolution level. options: standard | detailed
    "geometry_quality": "standard",  # Geometry quality level. options: standard | detailed
    "texture_alignment": "original_image",  # How the texture is aligned to the mesh. options: original_image | geometry
    "orientation": "default",  # Model orientation strategy. options: default | align_image
    "model_seed": 1,  # Seed for geometry generation reproducibility
    "texture_seed": 1,  # Seed for texture generation reproducibility
    "auto_size": False,  # Auto-scale the model to real-world dimensions (meters)
    "quad": False,  # Generate a quad mesh topology
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] == "completed":
            print("Generated image:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

image_url = check_status()

Install

Install the required package for your language.

pip install requests

Authentication

All API requests require authentication via an API key. You can get your API key from the Atlas Cloud dashboard.

export ATLASCLOUD_API_KEY="your-api-key-here"

HTTP Headers

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Keep your API key secure

Never expose your API key in client-side code or public repositories. Use environment variables or a backend proxy instead.

Submit a request

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Submit a Request

Submit an asynchronous generation request. The API returns a prediction ID that you can use to check the status and retrieve the result.

POST/api/v1/model/generateImage

Request Body

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "tripo-h3.1/image-to-3d",
    "prompt": "A beautiful landscape with mountains and lake"
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['data']['id']}")
print(f"Status: {result['data']['status']}")

Response

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

Check Status

Poll the prediction endpoint to check the current status of your request.

GET/api/v1/model/prediction/{prediction_id}

Polling Example

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Status Values

processingThe request is still being processed.

completedGeneration is complete. Outputs are available.

succeededGeneration succeeded. Outputs are available.

failedGeneration failed. Check the error field.

Completed Response

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.png"
    ],
    "metrics": {
      "predict_time": 8.3
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Upload Files

Upload files to Atlas Cloud storage and get a URL you can use in your API requests. Use multipart/form-data to upload.

POST/api/v1/model/uploadMedia

Upload Example

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Response

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Input Schema

The following parameters are accepted in the request body.

Total: 13Required: 2Optional: 11

modelstringrequired

model name

Default: "tripo-h3.1/image-to-3d"

image_urlstringrequired

URL of the input image used to create the 3D model.

face_limitinteger

Target mesh face count. If unset, the model determines it adaptively.

Min: 1000Max: 2000000

textureboolean

Enable texture generation.

Default: true

pbrboolean

Generate PBR (Physically Based Rendering) materials.

Default: true

texture_qualitystring

Texture resolution level.

Default: "standard"

standarddetailed

geometry_qualitystring

Geometry quality level.

Default: "standard"

standarddetailed

texture_alignmentstring

How the texture is aligned to the mesh.

Default: "original_image"

original_imagegeometry

orientationstring

Model orientation strategy.

Default: "default"

defaultalign_image

model_seedinteger

Seed for geometry generation reproducibility.

texture_seedinteger

Seed for texture generation reproducibility.

auto_sizeboolean

Auto-scale the model to real-world dimensions (meters).

Default: false

quadboolean

Generate a quad mesh topology. Outputs FBX when enabled.

Default: false

Example Request Body

{
  "model": "tripo-h3.1/image-to-3d",
  "image_url": "example_image_url",
  "texture": true,
  "pbr": true,
  "texture_quality": "standard",
  "geometry_quality": "standard",
  "texture_alignment": "original_image",
  "orientation": "default",
  "auto_size": false,
  "quad": false
}

Output Schema

The API returns a prediction response with the generated output URLs.

modelstring

model name

Default: "tripo-h3.1/image-to-3d"

created_atstring

ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z").

idstring

Unique identifier for the prediction.

outputsarray

Array of URLs to the generated 3D model files (GLB/FBX and variants), preview image last. Null when status is not completed.

filesarray

Structured per-output metadata (URL, type, content_type, file_name, file_size) for each generated 3D file.

thumbnailstring

Preview render image URL of the generated 3D model.

statusstring

Status of the task: created, processing, completed, or failed.

Example Response

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.png"
  ],
  "metrics": {
    "predict_time": 8.3
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integrates 400+ AI models directly into your AI coding assistant. One command to install, then use natural language to generate images, videos, and chat with LLMs.

Supported Clients

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ supported clients

Install

npx skills add AtlasCloudAI/atlas-cloud-skills

Setup API Key

Get your API key from the Atlas Cloud dashboard and set it as an environment variable.

export ATLASCLOUD_API_KEY="your-api-key-here"

Capabilities

Once installed, you can use natural language in your AI assistant to access all Atlas Cloud models.

Image GenerationGenerate images with models like Nano Banana 2, Z-Image, and more.

Video CreationCreate videos from text or images with Kling, Vidu, Veo, etc.

LLM ChatChat with Qwen, DeepSeek, and other large language models.

Media UploadUpload local files for image editing and image-to-video workflows.

Learn more

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server connects your IDE with 400+ AI models via the Model Context Protocol. Works with any MCP-compatible client.

Supported Clients

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ supported clients

Install

npx -y atlascloud-mcp

Configuration

Add the following configuration to your IDE's MCP settings file.

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Available Tools

atlas_generate_imageGenerate images from text prompts.

atlas_generate_videoCreate videos from text or images.

atlas_chatChat with large language models.

atlas_list_modelsBrowse 400+ available AI models.

atlas_quick_generateOne-step content creation with auto model selection.

atlas_upload_mediaUpload local files for API workflows.

Learn more

github.com/AtlasCloudAI/mcp-server

API Schema

{
  "info": {
    "description": "The AtlasCloud API.",
    "title": "AtlasCloud API",
    "version": "1.0.0"
  },
  "paths": {
    "/api/v1/model/generateImage": {
      "post": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        },
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        },
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "schema": {
              "type": "string",
              "description": "Request ID"
            },
            "required": true
          }
        ]
      },
      "x-api-name": "model_result"
    }
  },
  "openapi": "3.0.0",
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ],
  "components": {
    "schemas": {
      "Input": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "tripo-h3.1/image-to-3d"
          },
          "image_url": {
            "description": "URL of the input image used to create the 3D model.",
            "type": "string",
            "x-format": "image",
            "x-ui-component": "uploader"
          },
          "face_limit": {
            "description": "Target mesh face count. If unset, the model determines it adaptively.",
            "type": "integer",
            "minimum": 1000,
            "maximum": 2000000
          },
          "texture": {
            "description": "Enable texture generation.",
            "type": "boolean",
            "default": true
          },
          "pbr": {
            "description": "Generate PBR (Physically Based Rendering) materials.",
            "type": "boolean",
            "default": true
          },
          "texture_quality": {
            "description": "Texture resolution level.",
            "type": "string",
            "default": "standard",
            "enum": [
              "standard",
              "detailed"
            ],
            "x-ui-component": "select"
          },
          "geometry_quality": {
            "description": "Geometry quality level.",
            "type": "string",
            "default": "standard",
            "enum": [
              "standard",
              "detailed"
            ],
            "x-ui-component": "select"
          },
          "texture_alignment": {
            "description": "How the texture is aligned to the mesh.",
            "type": "string",
            "default": "original_image",
            "enum": [
              "original_image",
              "geometry"
            ],
            "x-ui-component": "select"
          },
          "orientation": {
            "description": "Model orientation strategy.",
            "type": "string",
            "default": "default",
            "enum": [
              "default",
              "align_image"
            ],
            "x-ui-component": "select"
          },
          "model_seed": {
            "description": "Seed for geometry generation reproducibility.",
            "type": "integer"
          },
          "texture_seed": {
            "description": "Seed for texture generation reproducibility.",
            "type": "integer"
          },
          "auto_size": {
            "description": "Auto-scale the model to real-world dimensions (meters).",
            "type": "boolean",
            "default": false
          },
          "quad": {
            "description": "Generate a quad mesh topology. Outputs FBX when enabled.",
            "type": "boolean",
            "default": false
          }
        },
        "required": [
          "model",
          "image_url"
        ],
        "type": "object",
        "x-order-properties": [
          "model",
          "image_url",
          "texture",
          "pbr",
          "texture_quality",
          "geometry_quality",
          "texture_alignment",
          "orientation",
          "face_limit",
          "model_seed",
          "texture_seed",
          "auto_size",
          "quad"
        ]
      },
      "PredictionResponse": {
        "properties": {
          "model": {
            "type": "string",
            "description": "model name",
            "default": "tripo-h3.1/image-to-3d"
          },
          "created_at": {
            "description": "ISO timestamp of when the request was created (e.g., \"2023-04-01T12:34:56.789Z\").",
            "format": "date-time",
            "type": "string"
          },
          "id": {
            "description": "Unique identifier for the prediction.",
            "type": "string"
          },
          "outputs": {
            "description": "Array of URLs to the generated 3D model files (GLB/FBX and variants), preview image last. Null when status is not completed.",
            "items": {
              "type": "string"
            },
            "nullable": true,
            "type": "array"
          },
          "files": {
            "description": "Structured per-output metadata (URL, type, content_type, file_name, file_size) for each generated 3D file.",
            "type": "array",
            "items": {
              "type": "object",
              "properties": {
                "url": {
                  "type": "string"
                },
                "type": {
                  "type": "string"
                },
                "content_type": {
                  "type": "string"
                },
                "file_name": {
                  "type": "string"
                },
                "file_size": {
                  "type": "integer"
                }
              }
            }
          },
          "thumbnail": {
            "description": "Preview render image URL of the generated 3D model.",
            "type": "string"
          },
          "status": {
            "description": "Status of the task: created, processing, completed, or failed.",
            "type": "string"
          },
          "urls": {
            "description": "Object containing related API endpoints.",
            "type": "object"
          }
        },
        "type": "object"
      }
    },
    "securitySchemes": {
      "apiKeyAuth": {
        "in": "header",
        "name": "Authorization",
        "type": "apiKey"
      }
    }
  }
}

LLM-Friendly Prompt Template

# tripo-h3.1/image-to-3d

> Generate high-quality 3D models from a single image using Tripo H3.1.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `tripo-h3.1/image-to-3d`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  model name
  - Default: `"tripo-h3.1/image-to-3d"`

- **`image_url`** (`string`, _required_):
  URL of the input image used to create the 3D model.

- **`texture`** (`boolean`, _optional_):
  Enable texture generation.
  - Default: `true`

- **`pbr`** (`boolean`, _optional_):
  Generate PBR (Physically Based Rendering) materials.
  - Default: `true`

- **`texture_quality`** (`string`, _optional_):
  Texture resolution level.
  - Default: `"standard"`
  - Options: "standard", "detailed"

- **`geometry_quality`** (`string`, _optional_):
  Geometry quality level.
  - Default: `"standard"`
  - Options: "standard", "detailed"

- **`texture_alignment`** (`string`, _optional_):
  How the texture is aligned to the mesh.
  - Default: `"original_image"`
  - Options: "original_image", "geometry"

- **`orientation`** (`string`, _optional_):
  Model orientation strategy.
  - Default: `"default"`
  - Options: "default", "align_image"

- **`face_limit`** (`integer`, _optional_):
  Target mesh face count. If unset, the model determines it adaptively.
  - Min: 1000
  - Max: 2000000

- **`model_seed`** (`integer`, _optional_):
  Seed for geometry generation reproducibility.

- **`texture_seed`** (`integer`, _optional_):
  Seed for texture generation reproducibility.

- **`auto_size`** (`boolean`, _optional_):
  Auto-scale the model to real-world dimensions (meters).
  - Default: `false`

- **`quad`** (`boolean`, _optional_):
  Generate a quad mesh topology. Outputs FBX when enabled.
  - Default: `false`



**Required Parameters Example**:

```json
{
  "model": "tripo-h3.1/image-to-3d",
  "image_url": ""
}
```


**Full Example**:

```json
{
  "model": "tripo-h3.1/image-to-3d",
  "image_url": "",
  "texture": true,
  "pbr": true,
  "texture_quality": "standard",
  "geometry_quality": "standard",
  "texture_alignment": "original_image",
  "orientation": "default",
  "face_limit": 1000,
  "model_seed": 0,
  "texture_seed": 0,
  "auto_size": false,
  "quad": false
}
```


### Output Schema

The API returns the following output format:


- **`model`** (`string`, _optional_):
  model name
  - Default: `"tripo-h3.1/image-to-3d"`

- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created (e.g., "2023-04-01T12:34:56.789Z").

- **`id`** (`string`, _optional_):
  Unique identifier for the prediction.

- **`outputs`** (`array[string]`, _optional_):
  Array of URLs to the generated 3D model files (GLB/FBX and variants), preview image last. Null when status is not completed.

- **`files`** (`array[object]`, _optional_):
  Structured per-output metadata (URL, type, content_type, file_name, file_size) for each generated 3D file.
  - Item properties:
    - **`url`** (`string`, _optional_):

    - **`type`** (`string`, _optional_):

    - **`content_type`** (`string`, _optional_):

    - **`file_name`** (`string`, _optional_):

    - **`file_size`** (`integer`, _optional_):


- **`thumbnail`** (`string`, _optional_):
  Preview render image URL of the generated 3D model.

- **`status`** (`string`, _optional_):
  Status of the task: created, processing, completed, or failed.

- **`urls`** (`object`, _optional_):
  Object containing related API endpoints.



**Example Response**:

```json
{
  "model": "tripo-h3.1/image-to-3d",
  "created_at": "",
  "id": "",
  "outputs": [
    ""
  ],
  "files": [
    {
      "url": "",
      "type": "",
      "content_type": "",
      "file_name": "",
      "file_size": 0
    }
  ],
  "thumbnail": "",
  "status": "",
  "urls": {}
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "tripo-h3.1/image-to-3d",
  "image_url": "",
  "texture": true,
  "pbr": true,
  "texture_quality": "standard",
  "geometry_quality": "standard",
  "texture_alignment": "original_image",
  "orientation": "default",
  "face_limit": 1000,
  "model_seed": 0,
  "texture_seed": 0,
  "auto_size": false,
  "quad": false
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/tripo-h3.1/image-to-3d)

No 3D output

Tripo H3.1 Image-to-3D

Tripo H3.1 Image-to-3D reconstructs a textured 3D model from a single input image. It produces a mesh (GLB, or FBX when quad topology is requested) with optional PBR materials and a preview render — ideal for turning product photos, concept art, or references into 3D assets.

🌟 Key Features

🖼 Image-to-3D

Reconstruct a complete 3D mesh from one input image.

🎨 Textures & PBR Materials

Optionally generate textures and Physically Based Rendering (PBR) materials for production-ready shading.

🎯 Texture Alignment & Orientation

Align the texture to the original image or to the geometry, and control model orientation.

🔺 Adaptive or Fixed Face Count

Let the model choose the mesh density adaptively, or cap it with face_limit for lighter assets.

📦 Multiple Output Formats

Receive GLB by default, or FBX when quad is enabled, alongside base and PBR model variants.

⚙️ Parameters

Parameter	Required	Description
`image_url`	✅	URL of the input image used to create the 3D model.
`texture`	❌	Enable texture generation (default: `true`).
`pbr`	❌	Generate PBR materials (default: `true`).
`texture_quality`	❌	Texture resolution level: `standard` (default) or `detailed`.
`geometry_quality`	❌	Geometry quality level: `standard` (default) or `detailed`.
`texture_alignment`	❌	Texture alignment: `original_image` (default) or `geometry`.
`orientation`	❌	Model orientation: `default` or `align_image`.
`face_limit`	❌	Target polygon count. Adaptive if unset.
`model_seed`	❌	Seed for geometry generation reproducibility.
`texture_seed`	❌	Seed for texture generation reproducibility.
`auto_size`	❌	Auto-scale the model to real-world dimensions in meters (default: `false`).
`quad`	❌	Generate a quad mesh topology; outputs FBX (default: `false`).

💲 Pricing

Configuration	Price
Without textures	$0.22
With standard textures	$0.33
With HD textures	$0.44
+ Detailed geometry (`geometry_quality: detailed`)	+$0.22
+ Quad mesh (`quad: true`)	+$0.055

💡 Input Tips

Use a clear, well-lit image with the subject centered and a clean background.
Set texture_alignment: original_image to keep the reconstructed texture faithful to the photo.
Use orientation: align_image when the model should face the same direction as the input.
Enable pbr for realistic materials in engines that support PBR shading.
Set face_limit to keep meshes lightweight for real-time / mobile use.

🎯 Use Cases

Product Digitization — Turn product photos into interactive 3D previews.
Game & AR Assets — Convert concept art or references into usable meshes.
Photogrammetry Alternative — Generate 3D from a single image without a full capture rig.
Rapid Prototyping — Explore 3D reconstructions before manual modeling.

📝 Notes

image_url is the only required field (besides model). Our platform also accepts image as an alias.
Output model files are returned in outputs (GLB/FBX and variants); the preview image is in thumbnail and appended to outputs last.
Structured per-file metadata (type, content_type, size) is available in files.
Task status values: created, processing, completed, failed.

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