AdaptiveMotorControlLab

AdaptiveMotorControlLab / FMPose3D

Public

A monocular 3D pose estimation algorithm for humans and other animals

xiu-cs.github.ioFMPose3D 3d-pose-estimation deeplabcut pose-estimation
80
10
100% credibility
Found Feb 09, 2026 at 48 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

FMPose3D is a user-friendly tool and Python package that estimates detailed 3D poses of humans and animals from single 2D images or videos.

How It Works

1
📸 Discover FMPose3D

You hear about a cool tool that turns everyday photos into lifelike 3D body poses for people and animals.

2
🛠️ Set it up simply

Install the tool on your computer with one easy command, no tech headaches.

3
📥 Grab ready models

Download pre-made smart models that already understand human and animal poses.

4
🖼️ Add your photos

Drop your pictures of people or animals into a simple folder.

5
✨ Run the demo

Click to launch and watch as flat images transform into spinning 3D skeletons right before your eyes.

🎉 Perfect 3D poses!

Celebrate seeing accurate, interactive 3D body reconstructions you can analyze or share instantly.

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Star Growth

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AI-Generated Review

What is FMPose3D?

FMPose3D is a Python package for monocular 3D pose estimation, lifting 2D keypoints from a single image into full 3D skeletons for humans and animals. It uses a flow matching algorithm to generate multiple plausible 3D hypotheses quickly via ODE sampling, then aggregates them for accurate results—ideal for monocular biomechanics github projects or monocular human pose estimation tasks. Drop in pretrained models via pip, run demos on in-the-wild images, and get visualized 3D outputs with simple shell scripts.

Why is it gaining traction?

It stands out in monocular 3D pose estimation and tracking by detection with SOTA accuracy on Human3.6M and Animal3D benchmarks, beating alternatives in speed thanks to few-step sampling—no need for heavy multi-view setups or slow optimization. Developers dig the plug-and-play demos for monocular depth estimation-like workflows, plus animal support opens doors beyond human-only tools like OpenPose.

Who should use this?

Biomechanics researchers analyzing animal locomotion from monocular camera pose estimation footage, CV engineers building monocular SLAM or visual odometry prototypes, or AR devs needing quick 3D reconstruction github pipelines for uncooperative subjects like wildlife.

Verdict

Promising for monocular 3D reconstruction github experiments, but at 48 stars and 1.0% credibility, it's early—solid preprint and demos, but expect tweaks for production. Try the animal demos if you're in monocular distance estimation github territory.

(178 words)

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