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

Imagem para 3D

Tripo H3.1 Image-to 3D API

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

Image-to-3d

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

Entrada

Image url *

Arraste arquivos aqui ou clique para fazer upload

MAX:1

Texture

Pbr

Texture quality

Geometry quality

Texture alignment

Orientation

Configurações Avançadas

Saída

Inativo

Seu modelo 3D gerado aparecerá aqui

Configure os parâmetros e clique em Run para começar

Cada execução custará $0.1. Com $10 você pode executar aproximadamente 100 vezes.

Parâmetros

Exemplo de código
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()

Instalar

Instale o pacote necessário para a sua linguagem de programação.

pip install requests

Autenticação

Todas as solicitações de API requerem autenticação por meio de uma chave de API. Você pode obter sua chave de API no painel do Atlas Cloud.

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

Cabeçalhos HTTP

import os

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

Mantenha sua chave de API segura

Nunca exponha sua chave de API em código do lado do cliente ou repositórios públicos. Use variáveis de ambiente ou um proxy de backend.

Enviar uma solicitação

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())

Enviar uma solicitação

Envie uma solicitação de geração assíncrona. A API retorna um ID de predição que você pode usar para verificar o status e obter o resultado.

POST/api/v1/model/generateImage

Corpo da solicitação

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']}")

Resposta

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

Verificar status

Consulte o endpoint de predição para verificar o status atual da sua solicitação.

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

Exemplo de polling

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)

Valores de status

processingA solicitação ainda está sendo processada.

completedA geração está completa. As saídas estão disponíveis.

succeededA geração foi bem-sucedida. As saídas estão disponíveis.

failedA geração falhou. Verifique o campo de erro.

Resposta concluída

{
  "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"
  }
}

Enviar arquivos

Envie arquivos para o armazenamento do Atlas Cloud e obtenha uma URL que pode ser usada nas suas solicitações de API. Use multipart/form-data para enviar.

POST/api/v1/model/uploadMedia

Exemplo de upload

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}")

Resposta

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

Schema de entrada

Os seguintes parâmetros são aceitos no corpo da solicitação.

Total: 13Obrigatório: 2Opcional: 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

Exemplo de corpo da solicitação

{
  "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
}

Schema de saída

A API retorna uma resposta de predição com as URLs de saída geradas.

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.

Exemplo de resposta

{
  "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

O Atlas Cloud Skills integra mais de 400 modelos de IA diretamente no seu assistente de codificação com IA. Um comando para instalar e depois use linguagem natural para gerar imagens, vídeos e conversar com LLM.

Clientes compatíveis

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ clientes compatíveis

Instalar

npx skills add AtlasCloudAI/atlas-cloud-skills

Configurar chave de API

Obtenha sua chave de API no painel do Atlas Cloud e defina-a como variável de ambiente.

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

Funcionalidades

Após a instalação, você pode usar linguagem natural no seu assistente de IA para acessar todos os modelos do Atlas Cloud.

Geração de imagensGere imagens com modelos como Nano Banana 2, Z-Image e mais.

Criação de vídeosCrie vídeos a partir de texto ou imagens com Kling, Vidu, Veo, etc.

Chat com LLMConverse com Qwen, DeepSeek e outros grandes modelos de linguagem.

Upload de mídiaEnvie arquivos locais para fluxos de trabalho de edição de imagens e imagem para vídeo.

Saiba mais

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

O Atlas Cloud MCP Server conecta seu IDE com mais de 400 modelos de IA através do Model Context Protocol. Funciona com qualquer cliente compatível com MCP.

Clientes compatíveis

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ clientes compatíveis

Instalar

npx -y atlascloud-mcp

Configuração

Adicione a seguinte configuração ao arquivo de configuração de MCP do seu IDE.

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

Ferramentas disponíveis

atlas_generate_imageGere imagens a partir de prompts de texto.

atlas_generate_videoCrie vídeos a partir de texto ou imagens.

atlas_chatConverse com grandes modelos de linguagem.

atlas_list_modelsExplore mais de 400 modelos de IA disponíveis.

atlas_quick_generateCriação de conteúdo em uma etapa com seleção automática de modelo.

atlas_upload_mediaEnvie arquivos locais para fluxos de trabalho de API.

Saiba mais

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"
      }
    }
  }
}

Template de Prompt Compatível com LLM

# 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

Carregando...

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.

Explorar Modelos Semelhantes

NEW

Texto para Imagem

Openai GPT Image 2 Text-to-Image

GPT Image 2 text to image is OpenAI's fast, cost-efficient text-to-image generator powered by GPT-5 guidance. Create photorealistic shots, product renders, concept art, and stylized graphics from natural-language prompts (optionally conditioned with an image). Supports custom aspect ratios, seeds, negative prompts, hex color hints, and style presets. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Openai GPT Image 2 Edit

GPT Image 2 Edit is OpenAI's image model for precise, natural-language edits. Add/remove objects, swap backgrounds, retouch faces, adjust colors/lighting, edit text/graphics, crop/resize, and apply hex color control. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

GPT Image 2 Developer Edit

GPT Image 2 Developer Edit applies natural-language instructions to one or more reference images, with common aspect ratios and 1k, 2k, or supported 4k output tiers. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

GPT Image 2 Developer Text-to-Image

GPT Image 2 Developer Text-to-Image generates polished visuals from natural-language prompts, with common aspect ratios and 1k, 2k, or supported 4k output tiers. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Seedream v5.0 Pro Edit

ByteDance flagship next-generation image editing model. Supports up to 10 reference images while preserving identity, lighting, and color tones for professional-quality modifications.

Seedream v5.0 Pro Text-to-Image

ByteDance flagship next-generation image generation model with stronger prompt adherence, refined typography, and photorealistic detail. Single-image output at 1.5K and 2K tiers with JPEG and PNG support.

Nano Banana 2 Lite Edit Developer

Google's fastest and most cost-efficient Nano Banana image model for editing, applying natural-language edits and multi-image composition to up to 14 reference images with low latency.

Nano Banana 2 Lite Text-to-Image Developer

Google's fastest and most cost-efficient Nano Banana image model, turning natural-language text prompts into high-quality 1k images in as little as 4 seconds for rapid, high-volume generation.

Nano Banana 2 Lite Edit

Nano banana lite is the efficiency-focused model in the image generation family. Sub-2 second latency with cost-effective generation and editing, fast multi-turn local edits, and 14 supported aspect ratios.

Nano Banana 2 Lite Text-to-image

MAI-Image-2.5-Flash Text-to-image

Microsoft's fast, cost-optimized text-to-image generation model, creating high-quality images at lower cost using the same diffusion-based architecture as MAI-Image-2.5.

From

$0.03/imagem