# Hunyuan 3D Rapid Image-to 3D — Atlas Cloud API

> Tencent Hunyuan 3D Rapid (Express) — fast lightweight 3D mesh generation from a single image, with optional PBR materials. Outputs GLB/OBJ/USDZ/FBX/STL/MP4.

This is the machine-readable API reference for **Hunyuan 3D Rapid Image-to 3D** on Atlas Cloud,
a unified API platform for 400+ AI models across text, image, video, audio and 3D.

- **Model ID**: `tencent/hunyuan3d-rapid/image-to-3d`
- **Built by**: TENCENT
- **Modality**: Image
- **Model page**: https://www.atlascloud.ai/models/tencent/hunyuan3d-rapid/image-to-3d
- **API key**: https://www.atlascloud.ai/console/api-keys
- **Docs**: https://www.atlascloud.ai/docs

## Pricing on Atlas Cloud

- $0.02 per image
- Pay-as-you-go. No minimum spend, no subscription required.

> **These are the authoritative Atlas Cloud rates for this model.** Any price that
> appears in the vendor description further down refers to a different platform or
> a different model variant and does not apply here.

## Use this model from an AI agent

Atlas Cloud ships three first-party integration surfaces. All three authenticate
with the same API key via the `ATLASCLOUD_API_KEY` environment variable.

### MCP server

The official MCP server (`atlascloud-mcp`) exposes this model to any
MCP-compatible host — Claude Code, OpenAI Codex, Cursor, Gemini CLI, Goose,
Claude Desktop. One-line install:

```bash
# Claude Code
claude mcp add atlascloud -- npx -y atlascloud-mcp

# OpenAI Codex CLI
codex mcp add atlascloud -- npx -y atlascloud-mcp

# Gemini CLI
gemini mcp add atlascloud -- npx -y atlascloud-mcp

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

Then ask in plain English; the agent calls `atlas_generate_image` with `model: "tencent/hunyuan3d-rapid/image-to-3d"`.
The server fetches each model's schema and validates parameters before submitting,
so invalid requests fail fast without spending credits.

MCP docs: https://www.atlascloud.ai/docs/mcp-server

### Agent Skills

`atlas-cloud-skills` is a portable skill package (API reference, code templates in
Python / Node.js / cURL, model IDs with pricing) for Claude Code, Cursor, Codex and
12+ other agents:

```bash
npx skills add AtlasCloudAI/atlas-cloud-skills
export ATLASCLOUD_API_KEY="your-api-key"
```

Skills docs: https://www.atlascloud.ai/docs/skills

### CLI

The `atlas` binary runs Atlas Cloud from a terminal or CI script. Async media jobs
are polled and downloaded automatically (use `--no-download` when a script only
needs the output URLs):

```bash
# Install (Homebrew, npm, or shell installer)
brew install AtlasCloudAI/tap/atlascloud
# npm install -g atlascloud-cli
# curl -fsSL https://raw.githubusercontent.com/AtlasCloudAI/cli/main/install.sh | sh

atlas auth login
atlas generate image tencent/hunyuan3d-rapid/image-to-3d -p "Your prompt here"
```

CLI docs: https://www.atlascloud.ai/docs/cli

## HTTP API reference

- **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**: `tencent/hunyuan3d-rapid/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: `"tencent/hunyuan3d-rapid/image-to-3d"`

- **`image`** (`string`, _required_):
  Source image (URL or base64 data URI). Publicly accessible, valid >=24h. Unilateral resolution 128-5000px, <=4.5MB. Formats: JPG/PNG/JPEG/WEBP. Tips: simple background, single object filling >50% of the frame.

- **`enable_pbr`** (`boolean`, _optional_):
  Generate PBR material maps (metallic, roughness, normal textures).
  - Default: `false`

- **`enable_geometry`** (`boolean`, _optional_):
  Also output a separate geometry-only (untextured) mesh; the result file is delivered as GLB.
  - Default: `false`

- **`format`** (`string`, _optional_):
  Output 3D file format.
  - Default: `"GLB"`
  - Options: "GLB", "OBJ", "USDZ", "FBX", "STL", "MP4"



**Required Parameters Example**:

```json
{
  "model": "tencent/hunyuan3d-rapid/image-to-3d",
  "image": ""
}
```


**Full Example**:

```json
{
  "model": "tencent/hunyuan3d-rapid/image-to-3d",
  "image": "",
  "enable_pbr": false,
  "enable_geometry": false,
  "format": "GLB"
}
```


### Output Schema

The API returns the following output format:


- **`created_at`** (`string`, _optional_):
  ISO timestamp of when the request was created.

- **`id`** (`string`, _optional_):
  Unique identifier for the prediction (use it to poll the result endpoint).

- **`model`** (`string`, _optional_):
  Model ID used for the prediction.

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

- **`outputs`** (`array[string]`, _optional_):
  Generated 3D file URLs (primary mesh first, e.g. GLB; preview image appended last). Empty until status is completed. URLs are AtlasCloud-hosted.

- **`files`** (`array[object]`, _optional_):
  Structured per-file outputs with format and content type.
  - Item properties:
    - **`url`** (`string`, _optional_):

    - **`type`** (`string`, _optional_):
      e.g. GLB, OBJ, FBX, USDZ, STL, MTL

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

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

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


- **`thumbnail`** (`string`, _optional_):
  Preview image URL for the generated 3D model (empty when none).

- **`credits_consumed`** (`number`, _optional_):
  Upstream-reported usage amount for the job (auditing).

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



**Example Response**:

```json
{
  "created_at": "",
  "id": "",
  "model": "",
  "status": "",
  "outputs": [
    ""
  ],
  "files": [
    {
      "url": "",
      "type": "",
      "content_type": "",
      "file_name": "",
      "file_size": 0
    }
  ],
  "thumbnail": "",
  "credits_consumed": 0,
  "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": "tencent/hunyuan3d-rapid/image-to-3d",
  "image": "",
  "enable_pbr": false,
  "enable_geometry": false,
  "format": "GLB"
}'

# 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/tencent/hunyuan3d-rapid/image-to-3d)

## About this model

_Vendor-supplied description. Any pricing or endpoint mentioned below refers to_
_other platforms — use the Atlas Cloud values above._

#### 1. Introduction

**Hunyuan 3D** is a family of generative AI models from Tencent that produce high-resolution textured 3D meshes from text prompts or single images. This README applies to the following API model identifiers:

- `tencent/hunyuan3d-rapid/image-to-3d`
- `tencent/hunyuan3d-rapid/text-to-3d`
- `tencent/hunyuan3d-pro/image-to-3d`
- `tencent/hunyuan3d-pro/text-to-3d`

Developed by Tencent AI Lab as part of the broader Hunyuan multimodal model family, Hunyuan 3D is designed to bridge the gap between 2D content creation and production-ready 3D asset generation. The system reached commercial general availability on Tencent Cloud in late November 2025, positioning itself as a state-of-the-art alternative to systems such as Trellis, Direct3D, Shap-E, and TEXTure across both shape and texture generation tasks.

The four API identifiers above map to two underlying generation tiers. The `tencent/hunyuan3d-rapid/*` variants are distilled, latency-optimized models that complete generations in roughly 2–3 minutes with fixed mid-range polygon budgets and 1K textures, while the `tencent/hunyuan3d-pro/*` variants invoke the full model with configurable polygon counts (40K–1.5M) and up to 4K PBR textures. Within each tier, the `image-to-3d` and `text-to-3d` suffixes correspond to mutually exclusive input modalities accepted by the same backend job.

---

#### 2. Key Features & Innovations

- **Two-Stage Decoupled Pipeline**: The system separates geometry from appearance via **Hunyuan3D-DiT**, a flow-based diffusion transformer that generates the underlying shape, and **Hunyuan3D-Paint**, a multi-view diffusion model that synthesizes textures (with full PBR support starting from version 2.1). This decoupling allows independent optimization of each stage and supports texture re-painting on existing meshes.

_(Description truncated. Full text on the model page.)_

---

Atlas Cloud — one API for 400+ AI models. Model page: https://www.atlascloud.ai/models/tencent/hunyuan3d-rapid/image-to-3d · Docs: https://www.atlascloud.ai/docs