# Image-to-3D Generator (`amber_mandola/ia-3d-generator`) Actor

Upload a photo, get back a textured 3D mesh (GLB): fast object generation via SF3D or depth-based environment shells for rooms/scenes, with optional AI part-segmentation.

- **URL**: https://apify.com/amber\_mandola/ia-3d-generator.md
- **Developed by:** [Bahae El Hmimdi](https://apify.com/amber_mandola) (community)
- **Categories:**
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $500.00 / 1,000 image-to-3d generations

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

Turn any photo into a textured 3D model. Upload an image and get back a ready-to-use GLB mesh in seconds — no manual modeling, no multi-view capture, no photogrammetry rig required. Runs in [Standby mode](https://docs.apify.com/platform/actors/development/programming-interface/standby), so requests are answered directly and synchronously, with no run to start or poll.

### What does Image-to-3D Generator do?

This Actor wraps a single-image-to-3D pipeline: **object mode** uses SF3D for fast, high-fidelity mesh generation of a compact subject (product shot, character, prop), and **environment mode** treats the photo as a non-compact scene (a furnished room, an interior) — it estimates depth and builds a displaced 3D "shell" mesh that follows the room's actual layout. An optional add-on automatically segments a generated object into its individual parts (Hunyuan3D-Part / P3-SAM), useful for rigging, editing, or game-asset pipelines.

Because it runs in Standby mode on the Apify platform, you get monitoring, usage analytics, and pay-per-use billing on top of the underlying generation service, with no infrastructure of your own to manage.

### Why use Image-to-3D Generator?

- **Product visualization** — turn a single product photo into a 3D asset for AR/VR or web viewers.
- **Game and asset pipelines** — quickly block out props or environment pieces from reference photos, with automatic part-splitting for further editing.
- **Interior/architectural previews** — convert a room photo into a rough 3D shell for layout or renovation mockups.

### How to use Image-to-3D Generator

1. Open the Actor's Standby URL (shown on the **Standby** tab once the Actor is running) or call it directly at `https://<actor-id>.apify.actor` with your Apify token.
2. Send a `POST` request to `/generate` with `multipart/form-data`: an `image` file field, plus optional parameters (see Input below).
3. The response is JSON with a `model_url` — a relative path.
4. Fetch the actual GLB file with a `GET` request to that same Standby host at `/download/<token>`.

### Input

Sent as `multipart/form-data` fields on `POST /generate`:

| Field | Type | Default | Description |
|---|---|---|---|
| `image` | file | required | Source image (PNG, JPEG, or WebP). |
| `mode` | string | `object` | `object`: compact single subject (fast, SF3D). `environment`: non-compact scene/room via image+depth displacement. |
| `texture_resolution` | number | `512` | `mode=object` only. Baked texture resolution, e.g. 512, 1024, 2048. |
| `foreground_ratio` | number | `0.85` | `mode=object` only. How tightly the subject is cropped before baking. |
| `split_parts` | boolean | `false` | `mode=object` only. Also segment the generated mesh into parts via Hunyuan3D-Part (P3-SAM). |
| `part_threshold` | number | `0.95` | Only used when `split_parts=true`. |
| `depth_model` | string | `DepthPro (mflux)` | `mode=environment` only. Depth-estimation backend. |
| `empty_model` | string | `sdxl_turbo` | `mode=environment` only. Backend used to remove furniture before depth displacement. |
| `mesh_resolution` | number | `128` | `mode=environment` only. Room shell mesh grid resolution. |
| `depth_scale` | number | `2.5` | `mode=environment` only. Depth displacement strength. |

### Output

`POST /generate` returns:

```json
{
  "model_url": "/v1/threed/download/8c0144690bd2420a911fa1aae4693b2f",
  "elapsed_seconds": 29.6,
  "parts": null
}
```

`GET /download/<token>` (note: strip the `/v1/threed` prefix from `model_url`, the Actor forwards it internally) returns the raw GLB file — fetch it directly to save or display the mesh.

When `split_parts=true`, `parts` is a list of `{part_id, url, face_count, vertex_count}` objects, one per segmented part, each downloadable the same way.

### Pricing

Billed per call to `/generate` (a mesh generation) — downloading a result via `/download` is free. Object-mode generations typically finish in under a minute; environment mode is slower since it involves a full depth-estimation and furniture-removal pass.

### Tips

- Use `mode=object` for anything that's a single, isolated subject — it's the fast path.
- Reserve `mode=environment` for full-room/scene photos where you want a layout-following shell rather than a single compact mesh.
- `split_parts=true` only makes sense for `mode=object` results; it's ignored otherwise.

### Support

Something not working as expected? Open an issue on this Actor's Issues tab.

# Actor input Schema

## Actor input object example

```json
{}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("amber_mandola/ia-3d-generator").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("amber_mandola/ia-3d-generator").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call amber_mandola/ia-3d-generator --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,amber_mandola/ia-3d-generator"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/er2G6BDdcTQjMjj1W/builds/l56odX40gNEPRInxb/openapi.json