geopavlakos

HaMeR

About

HaMeR is an open-weight model from geopavlakos. Transformer-based 3D hand mesh recovery with ViTPose-H detection. It accepts text and images. License is —.

License
—
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

HaMeR is one of the catalog's pose models. The table is each model's published price, size, and context.

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

Benchmarks

Published scores for HaMeR, from the HaMeR paper.

Evals

FreiHAND F@5mm76.5
FreiHAND PA-MPJPE mm13

A lower score is better.

Reading	Example
Qualitative	Clear structure, grounded in the input
Quantitative	FreiHAND F@5mm: 76.5.
Cost and performance	Lower listed rate, mid-pack latency

Methods

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

Method	Returns
pose	Keypoints for each person

Estimate cost

Input tokensOutput tokens

$3.90

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="geopavlakos/hamer",
    messages=[{"role": "user", "content": "What is in this image?"}],
)

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