# meshy-6-image-to-3d > Generate a clean 3D mesh from a single reference image. Output is a textured .glb plus FBX/OBJ/USDZ alternatives. Optional PBR materials and rigging-ready output. ## Overview - **Endpoint**: `POST https://api.muapi.ai/api/v1/meshy-6-image-to-3d` - **Model ID**: `meshy-6-image-to-3d` - **Category**: image to 3d - **Variant**: Meshy 6 Image to 3D - **Family**: meshy - **Cost**: 0.5 credits per call (some models compute cost dynamically based on params) ## API Usage MuApi uses a **submit-then-poll** pattern: submit a job, get a `request_id`, then poll the predictions endpoint until `status` is `completed`. Optionally pass `?webhook=YOUR_URL` on the submit call to receive a POST callback when the job finishes (skip polling). **Authentication**: send your MuApi key in the `x-api-key` header. Get one at https://muapi.ai/access-keys. ### 1. Submit a job ```http POST https://api.muapi.ai/api/v1/meshy-6-image-to-3d Content-Type: application/json x-api-key: YOUR_API_KEY ``` **Minimum (required only):** ```json { "image_url": "https://example.com/your-image_url" } ``` **Full example (all params):** ```json { "image_url": "https://example.com/your-image_url", "should_texture": true, "topology": "triangle", "target_polycount": 30000, "should_remesh": true, "symmetry_mode": "auto", "enable_pbr": false, "pose_mode": "", "texture_prompt": "" } ``` **Response:** ```json { "request_id": "abc123", "status": "processing" } ``` ### 2. Poll for the result ```http GET https://api.muapi.ai/api/v1/predictions/{request_id}/result x-api-key: YOUR_API_KEY ``` Possible `status` values: `queued`, `pending`, `processing`, `completed`, `failed`, `cancelled`. Poll every 2-5 seconds until terminal. When `completed`, the result URLs are in the `outputs` array. **Example response when `completed`:** ```json { "id": "abc123", "status": "completed", "outputs": [ "https://cdn.muapi.ai/.../output.png" ], "urls": { "get": "https://api.muapi.ai/api/v1/predictions/abc123/result" }, "created_at": "2026-05-08T12:34:56Z", "has_nsfw_contents": [] } ``` ### cURL ```bash # 1. Submit REQUEST_ID=$(curl -s -X POST https://api.muapi.ai/api/v1/meshy-6-image-to-3d \ -H "x-api-key: $MUAPI_API_KEY" \ -H "Content-Type: application/json" \ -d '{"image_url":"https://example.com/your-image_url"}' | jq -r .request_id) # 2. Poll until completed while :; do RESP=$(curl -s https://api.muapi.ai/api/v1/predictions/$REQUEST_ID/result -H "x-api-key: $MUAPI_API_KEY") STATUS=$(echo "$RESP" | jq -r .status) [ "$STATUS" = "completed" ] && echo "$RESP" | jq .outputs && break [ "$STATUS" = "failed" ] && echo "$RESP" && exit 1 sleep 3 done ``` ### Python ```python import os, time, requests API = "https://api.muapi.ai/api/v1" headers = {"x-api-key": os.environ["MUAPI_API_KEY"]} r = requests.post(f"{API}/meshy-6-image-to-3d", headers=headers, json={"image_url":"https://example.com/your-image_url"}) request_id = r.json()["request_id"] while True: res = requests.get(f"{API}/predictions/{request_id}/result", headers=headers).json() if res["status"] == "completed": print(res["outputs"]); break if res["status"] == "failed": raise RuntimeError(res.get("error")); time.sleep(3) ``` ## Input Schema The API accepts the following input parameters: - **`image_url`** (`string`, _required_): Reference image used to generate the 3D model. - **`should_texture`** (`boolean`, _optional_): Generate textures alongside the mesh. - Default: `true` - **`topology`** (`string`, _optional_): Output mesh topology. - Default: `"triangle"` - Options: `"triangle"`, `"quad"` - **`target_polycount`** (`int`, _optional_): Target polygon count for the output mesh. - Default: `30000` - **`should_remesh`** (`boolean`, _optional_): Enable remesh phase. - Default: `true` - **`symmetry_mode`** (`string`, _optional_): Symmetry detection mode. - Default: `"auto"` - Options: `"off"`, `"auto"`, `"on"` - **`enable_pbr`** (`boolean`, _optional_): Generate PBR maps. - Default: `false` - **`pose_mode`** (`string`, _optional_): Force humanoid characters into a standard pose. - Default: `""` - Options: `""`, `"a-pose"`, `"t-pose"` - **`texture_prompt`** (`string`, _optional_): Optional text to guide the texturing pass. ## Output Schema The polling endpoint returns the following fields: - **`id`** (`string`): The request ID. - **`status`** (`string`): One of `queued`, `pending`, `processing`, `completed`, `failed`, `cancelled`. - **`outputs`** (`array`): URLs to generated images/videos/audio. Empty until `status` is `completed`. - **`urls.get`** (`string`): Self-link to re-fetch this prediction. - **`error`** (`string` | `null`): Error message if `status` is `failed`. - **`created_at`** (`string`): ISO-8601 timestamp of when the request was created. - **`has_nsfw_contents`** (`array of boolean`): Per-output NSFW detection flags. ## Webhooks (optional) Append `?webhook=https://your-server/path` to the submit URL. When the job reaches a terminal state, MuApi will POST the same shape as the polling response to your URL — no polling needed. ## Agent Integration MuApi ships an MCP server and CLI so agents (Claude Code, Cursor, custom) can call this endpoint without writing HTTP code: ```bash # Install the CLI npm install -g muapi-cli # Authenticate once muapi auth login # Expose all MuApi models as MCP tools to your agent muapi mcp serve ``` The MCP server exposes tools that wrap submit + poll for every model, including `meshy-6-image-to-3d`. See `muapi --help` for category-specific shortcuts (`muapi image generate`, `muapi video from-image`, etc.). ## Related Models - [Meshy 6 Image to 3D](https://muapi.ai/playground/meshy-6-image-to-3d) - [Meshy 6 Multi Image to 3D](https://muapi.ai/playground/meshy-6-multi-image-to-3d) - [Meshy 6 Text to 3D](https://muapi.ai/playground/meshy-6-text-to-3d) ## Resources - [Playground Page](https://muapi.ai/playground/meshy-6-image-to-3d) - [API Reference](https://muapi.ai/playground/meshy-6-image-to-3d?tab=2) - [Global llms.txt](https://muapi.ai/llms.txt)