2D → SimReady

Send one or more images of an object and Rigyd reconstructs it as a SimReady USD asset.

Method	POST
Path	/api/conversions/generate
Content-type	multipart/form-data
Job type	image_to_simready (1 image) · multiview_to_simready (2-4 images)
Credits	3

The same endpoint handles single-view and multiview based on the number of images[] you send. Provide multiple angles of the same object for sharper geometry.

Request fields

Field	Type	Required	Notes
images[]	file (1-4)	yes	.jpg, .jpeg, .png, .webp. Max 10 MB each.
face_limit	int	no	Cap output mesh face count.
model_version	string	no	Override generation model (advanced).
threshold	float	no	CoACD concavity threshold (advanced).
llm_provider	string	no	Override material-identification LLM (advanced).
skip_validation	boolean	no	Skip USD schema validation. Default false.

Caution

Do not send a prompt field on the same request — that triggers a 422. Use Text → SimReady for prompt-only generation.

Examples

Single image

curl

curl -X POST https://api.rigyd.com/api/conversions/generate \
  -H "Authorization: Bearer rgyd_live_..." \
  -F "images[]=@./toolbox-front.jpg"

JavaScript

import fs from 'node:fs';

const form = new FormData();
form.append('images[]', new Blob([fs.readFileSync('./toolbox-front.jpg')]), 'toolbox-front.jpg');

const res = await fetch('https://api.rigyd.com/api/conversions/generate', {
  method: 'POST',
  headers: { Authorization: `Bearer ${process.env.RIGYD_API_KEY}` },
  body: form,
});
const { data } = await res.json();

Python

import os, requests

with open("toolbox-front.jpg", "rb") as f:
    res = requests.post(
        "https://api.rigyd.com/api/conversions/generate",
        headers={"Authorization": f"Bearer {os.environ['RIGYD_API_KEY']}"},
        files={"images[]": ("toolbox-front.jpg", f, "image/jpeg")},
    )
job = res.json()["data"]

Multiview (2-4 images)

curl

curl -X POST https://api.rigyd.com/api/conversions/generate \
  -H "Authorization: Bearer rgyd_live_..." \
  -F "images[]=@./front.jpg" \
  -F "images[]=@./side.jpg" \
  -F "images[]=@./back.jpg"

JavaScript

import fs from 'node:fs';

const form = new FormData();
for (const name of ['front.jpg', 'side.jpg', 'back.jpg']) {
  form.append('images[]', new Blob([fs.readFileSync(name)]), name);
}

const res = await fetch('https://api.rigyd.com/api/conversions/generate', {
  method: 'POST',
  headers: { Authorization: `Bearer ${process.env.RIGYD_API_KEY}` },
  body: form,
});

Python

import os, requests

files = [
    ("images[]", (name, open(name, "rb"), "image/jpeg"))
    for name in ["front.jpg", "side.jpg", "back.jpg"]
]
res = requests.post(
    "https://api.rigyd.com/api/conversions/generate",
    headers={"Authorization": f"Bearer {os.environ['RIGYD_API_KEY']}"},
    files=files,
)

Response — 202 Accepted

{
  "data": {
    "id": "abc123...",
    "status": "queued",
    "filename": "toolbox-front.jpg",
    "progress": 0,
    "job_type": "multiview_to_simready",
    "credits_charged": 3,
    "createdAt": "2026-05-06T12:00:00.000Z"
  }
}

job_type is image_to_simready for a single image and multiview_to_simready for 2-4 images. Both cost 3 credits.

Tips

• More views = sharper geometry. Three to four images covering front / side / back / top yield noticeably better reconstructions than a single hero shot.
• Plain backgrounds help — the generation model isolates the object before reconstruction.
• One object per call. If your image contains multiple objects, the result is undefined.

Next: poll the job → download the USD.