Inference Logoinference shell

TokenRig

Automatic rigging for 3D models with TokenRig (SkinTokens). Predicts a skeleton and skin weights in one pass and keeps the original textures. GLB, glTF, FBX, OBJ in; GLB or FBX out.

3d rigging
run in browser →run via APIrun with your agent
maker
infsh
category
3d
runs via
api, javascript and python sdk, belt cli, mcp
updated
2026-10-28
price
GPU time, billed per millisecond

api reference

about

automatic rigging for 3d models with tokenrig (skintokens). predicts a skeleton and skin weights in one pass and keeps the original textures. glb, gltf, fbx, obj in; glb or fbx out.

get an api key

create one in settings → api keys. the examples read it from INFERENCE_API_KEY.

bash
1export INFERENCE_API_KEY="inf_your_key"

1. calling the api

install the client

nothing to install. any http client works; the examples use curl, and jq to read responses. every response wraps its result in a data field.

run and get result

send "wait": true to hold the connection until the task finishes. without it the call returns right away with the task still running.

bash
12# "wait": true holds the connection until the task finishes.3# the response is {"data": {"id", "status", "status_text", "output", ...}}4curl -X POST https://api.inference.sh/apps/run \5  -H "Authorization: Bearer $INFERENCE_API_KEY" \6  -H "Content-Type: application/json" \7  -d '{8    "app": "infsh/tokenrig",9    "input": {10      "model": "https://example.com/file.png"11    },12    "wait": true13  }'

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks. send "stream": true and the response is a server-sent event stream that ends when the task does.

bash
12# "stream": true answers with server-sent events until the task finishes3curl -N -X POST https://api.inference.sh/apps/run \4  -H "Authorization: Bearer $INFERENCE_API_KEY" \5  -H "Content-Type: application/json" \6  -d '{7    "app": "infsh/tokenrig",8    "input": {9      "model": "https://example.com/file.png"10    },11    "stream": true12  }'

run in the background

start the task and get its id back right away, then check on it later. for long tasks, or when you'd rather be called back, see webhooks.

bash
1# without "wait" the call returns the task right away (status 1, "received")2TASK_ID=$(curl -s -X POST https://api.inference.sh/apps/run \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{6    "app": "infsh/tokenrig",7    "input": {8      "model": "https://example.com/file.png"9    }10  }' | jq -r '.data.id')1112# later: read its state. done when status is 10 (completed), 11 (failed) or 12 (cancelled)13curl -s https://api.inference.sh/tasks/$TASK_ID \14  -H "Authorization: Bearer $INFERENCE_API_KEY" | jq '.data | {status, status_text, output}'

2. authentication

the api uses api keys for authentication. see the authentication docs for detailed setup instructions.

3. files

file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.

automatic upload

pass urls directly or use data uris for inline content.

bash
1# pass URLs or data URIs as input values2curl -X POST https://api.inference.sh/apps/run \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{6    "app": "infsh/tokenrig",7    "input": {8      "image": "https://example.com/image.png",9      "audio": "data:audio/mp3;base64,..."10    },11    "wait": true12  }'

manual upload

you can also upload files manually and use the returned url.

bash
1# 1. create the file: the response has an upload_url and the file's uri2FILE=$(curl -s -X POST https://api.inference.sh/files \3  -H "Authorization: Bearer $INFERENCE_API_KEY" \4  -H "Content-Type: application/json" \5  -d '{"files": [{"filename": "image.png", "content_type": "image/png"}]}')67# 2. PUT the bytes to upload_url8curl -X PUT "$(echo "$FILE" | jq -r '.data[0].upload_url')" \9  -H "Content-Type: image/png" \10  --data-binary @image.png1112# 3. pass the uri as an input value13echo "$FILE" | jq -r '.data[0].uri'

4. webhooks

get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.

bash
1curl -X POST https://api.inference.sh/apps/run \2  -H "Authorization: Bearer $INFERENCE_API_KEY" \3  -H "Content-Type: application/json" \4  -d '{5    "app": "infsh/tokenrig",6    "input": {7        "model": "https://example.com/file.png"8    },9    "webhook": "https://your-server.com/webhook"10  }'

webhook payload

your endpoint receives a JSON POST with the task result:

json
1{2  "event": "task.completed",3  "timestamp": "2024-01-15T10:30:05Z",4  "data": {5    "id": "task_abc123",6    "short_id": "abc123",7    "status": 10,8    "status_text": "completed",9    "output": { ... },10    "created_at": "2024-01-15T10:30:00Z",11    "updated_at": "2024-01-15T10:30:05Z"12  }13}
eventstringtask.completed, task.failed or task.cancelled
timestampstringiso timestamp of the delivery
data.idstringtask id
data.short_idstringshort task id
data.statusnumberterminal status (10=completed, 11=failed, 12=cancelled, 13=rejected)
data.status_textstringthe status as a word: completed, failed or cancelled
data.outputobjecttask output (when completed)
data.errorstringerror message (when failed, omitted otherwise)
data.session_idstringsession id (when the task ran in a session, omitted otherwise)
data.created_atstringiso timestamp
data.updated_atstringiso timestamp

5. schema

input

modelstring(file)*

the 3d model to rig: glb, gltf, fbx, obj, dae or .blend.

output_formatstring

file format of the rigged model.

default: "glb"
options:"glb""fbx"
keep_originalboolean

put the rig onto the original model, keeping its textures and materials. off exports the mesh the model rigged.

default: true
smooth_skinboolean

clean up the skin weights with a voxel pass.

default: false
num_beamsinteger

beam search width. higher is slower and often better.

default: 10min:1max:20
top_kinteger

sampling top-k.

default: 5min:1max:200
top_pnumber

sampling top-p.

default: 0.95min:0.1max:1
temperaturenumber

sampling temperature.

default: 1min:0.1max:2
repetition_penaltynumber

repetition penalty.

default: 2min:0.5max:3
seedinteger

seed. empty: random.

min:0

output

modelstring(file)*

the rigged model, with its skeleton and skin weights.

other 3d apps

Tripo Auto Rig

tripo/tripo-rig

$0.01/credit (rig check free, auto rig 25 credits, retarget 10 credits/animation)

Hunyuan 3D Auto Rigging

tencent/hunyuan-3d-auto-rigging

$0.02/credit, 10 credits/call ($0.20/call)

Hunyuan 3D 3.1

tencent/hunyuan-3d-3-1

$0.02/credit; Geometry 15cr, Normal 25cr, +10cr views/PBR/face-count

Rodin Gen-2.5

hyper3d/rodin-gen-2-5

$0.58/credit; Extreme-High tier +0.5 credits, texture extreme-high +2 credits

Meshy 7.1

meshy/meshy-7-1

$0.0127/Meshy credit (20 base + 10-15 texture + 5 hi-res geo)

more from infsh

HTTP Request

infsh/http

from $0.00000277/sec

DramaBox Text-to-Speech

infsh/dramabox

GPU time, billed per millisecond

Markdown to Word Converter

infsh/markdown-to-word

from $0.00000277/sec

Harrier OSS v1 Text Embeddings

infsh/harrier-oss-v1

GPU time, billed per millisecond

new models

  1. Tripo Auto Rig

    tripo/tripo-rig

    3d$0.01/credit (rig check free, auto rig 25 credits, retarget 10 credits/animation)

  2. Hunyuan 3D Auto Rigging

    tencent/hunyuan-3d-auto-rigging

    3d$0.02/credit, 10 credits/call ($0.20/call)

  3. Hunyuan 3D 3.1

    tencent/hunyuan-3d-3-1

    3d$0.02/credit; Geometry 15cr, Normal 25cr, +10cr views/PBR/face-count

  4. Rodin Gen-2.5

    hyper3d/rodin-gen-2-5

    3d$0.58/credit; Extreme-High tier +0.5 credits, texture extreme-high +2 credits

  5. Meshy 7.1

    meshy/meshy-7-1

    3d$0.0127/Meshy credit (20 base + 10-15 texture + 5 hi-res geo)

ready to run TokenRig?

try in browserbrowse all apps

we use cookies

we use cookies to ensure you get the best experience on our website. for more information on how we use cookies, please see our cookie policy.

by clicking "accept", you agree to our use of cookies.
learn more.