USYD Community

ViTPose+ Large

About

ViTPose+ Large is a 434M open-weight model from USYD Community. 2D human pose estimation (ViT-L, 434M). It accepts text, images, and video. License is Apache-2.0. Released January 12, 2025.

Parameters
434M
Published parameter count.
License
Apache-2.0
License on the weights.

Pricing

Token rates are US dollars per 1M tokens. Image rates are per 1K images. Audio rates are per 1M audio seconds.

Rate	Price
Input / 1M	$3.90
Cached input / 1M	$3.90
Output / 1M	$0

Compare

ViTPose+ Large is the smallest pose model here, at 434M, and the one that accepts video.

Model	What it does	Price	Parameters	Context
ViTPose+ Large	2D human pose estimation (ViT-L, 434M).	$3.90 / 1M input	434M	—
HaMeR	Transformer-based 3D hand mesh recovery with ViTPose-H detection.	$3.90 / 1M input	—	—

Benchmarks

Published scores for ViTPose+ Large, from the ViTPose repository.

Evals

COCO AP78.6
COCO AR84.1

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	COCO AP: 78.6.
Cost and performance	Lower listed rate, mid-pack latency

Examples

2D pose estimation
ViTPose-Plus emits 17 keypoints per person, COCO schema.
{"poses":[{"keypoints":[[x,y,c], 17 of...]}], ...}

Methods

Methods this model serves. Payload shapes are in the docs.

Method	Returns
pose	Keypoints for each person

Estimate cost

Estimate. An hour of video is 15 frames a minute at 256 tokens a frame, plus the audio in that request, and 500 output tokens a minute.

Hours of video

$1,347.84

Quick start

Call this model on the OpenAI-compatible gateway. The model id is already filled in.

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.chat.completions.create(
    model="usyd-community/vitpose-plus-large",
    messages=[{"role": "user", "content": "What is in this image?"}],
)

print(response.choices[0].message.content)